diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..bc579b6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,93 @@
+.DS_Store
+.floydexpt
+.floydignore
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..6f5b1ae
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,22 @@
+dist: xenial
+language: python
+python:
+  - "3.7"
+install:
+  - sudo apt-get update
+  # We do this conditionally because it saves us some downloading if the
+  # version is the same.
+  - wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  # Useful for debugging any issues with conda
+  - conda info -a
+
+  - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION -f environment.yml
+  - source activate test-environment
+
+script:
+  - travis_wait 30 py.test -v
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..065c1cf
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,37 @@
+COPYRIGHT
+
+All contributions by Francesco Mosconi:
+Copyright (c) 2017, Francesco Mosconi.
+All rights reserved.
+
+All contributions by Catalit LLC:
+Copyright (c) 2017, Catalit LLC.
+All rights reserved.
+
+All other contributions:
+Copyright (c) 2015, the respective contributors.
+All rights reserved.
+
+Each contributor holds copyright over their respective contributions.
+The project versioning (Git) records all such contribution source information.
+MIT License
+
+Copyright (c) 2017 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/course/0_Check_Environment.ipynb b/course/0_Check_Environment.ipynb
new file mode 100644
index 0000000..2e0e104
--- /dev/null
+++ b/course/0_Check_Environment.ipynb
@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check Environment\n",
+    "This notebook checks that you have correctly created the environment and that all packages needed are installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment\n",
+    "\n",
+    "The next command should return a line like (Mac/Linux):\n",
+    "\n",
+    "    /<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/bin/python\n",
+    "\n",
+    "or like (Windows 10):\n",
+    "\n",
+    "    C:\\\\<YOUR-HOME-FOLDER>\\\\Anaconda3\\\\envs\\\\ztdl\\\\python.exe\n",
+    "\n",
+    "In particular you should make sure that you are using the python executable from within the course environment.\n",
+    "\n",
+    "If that's not the case do this:\n",
+    "\n",
+    "1. close this notebook\n",
+    "2. go to the terminal and stop jupyer notebook\n",
+    "3. make sure that you have activated the environment, you should see a prompt like:\n",
+    "\n",
+    "        (ztdl) $\n",
+    "4. (optional) if you don't see that prompt activate the environment:\n",
+    "    - mac/linux:\n",
+    "    \n",
+    "            conda activate ztdl\n",
+    "\n",
+    "    - windows:\n",
+    "\n",
+    "            activate ztdl\n",
+    "5. restart jupyter notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.executable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python 3.7\n",
+    "\n",
+    "The next line should say that you're using Python 3.7.x from Anaconda. At the time of publication it looks like this (Mac/Linux):\n",
+    "\n",
+    "    Python 3.7.3 (default, Mar 27 2019, 22:11:17)\n",
+    "    [GCC 7.3.0] :: Anaconda, Inc. on linux\n",
+    "    Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
+    "\n",
+    "or like this (Windows 10):\n",
+    "\n",
+    "    Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32\n",
+    "    Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
+    "\n",
+    "but date and exact version of GCC may change in the future.\n",
+    "\n",
+    "If you see a different version of python, go back to the previous step and make sure you created and activated the environment correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Check that Jupyter is running from within the environment. The next line should look like (Mac/Linux):\n",
+    "\n",
+    "    /<YOUR-HOME-FOLDER>/anaconda/envs/ztdl/lib/python3.6/site-packages/jupyter.py'\n",
+    "\n",
+    "or like this (Windows 10):\n",
+    "\n",
+    "    C:\\\\Users\\\\<YOUR-USER>\\\\Anaconda3\\\\envs\\\\ztdl\\\\lib\\\\site-packages\\\\jupyter.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jupyter\n",
+    "jupyter.__file__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other packages\n",
+    "\n",
+    "Here we will check that all the packages are installed and have the correct versions. If everything is ok you should see:\n",
+    "    \n",
+    "    Using TensorFlow backend.\n",
+    "    \n",
+    "    Houston we are go!\n",
+    "\n",
+    "If there's any issue here please make sure you have checked the previous steps and if it's all good please send us a question in the Q&A forum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pip\n",
+    "import numpy\n",
+    "import jupyter\n",
+    "import matplotlib\n",
+    "import sklearn\n",
+    "import scipy\n",
+    "import pandas\n",
+    "import PIL\n",
+    "import seaborn\n",
+    "import tensorflow\n",
+    "\n",
+    "\n",
+    "def check_version(pkg, version):\n",
+    "    actual = pkg.__version__.split('.')\n",
+    "    if len(actual) == 3:\n",
+    "        actual_major = '.'.join(actual[:2])\n",
+    "    elif len(actual) == 2:\n",
+    "        actual_major = '.'.join(actual)\n",
+    "    else:\n",
+    "        raise NotImplementedError(pkg.__name__ +\n",
+    "                                  \"actual version :\"+\n",
+    "                                  pkg.__version__)\n",
+    "    try:\n",
+    "        assert(actual_major == version)\n",
+    "    except Exception as ex:\n",
+    "        print(\"{} {}\\t=> {}\".format(pkg.__name__,\n",
+    "                                    version,\n",
+    "                                    pkg.__version__))\n",
+    "        raise ex\n",
+    "\n",
+    "check_version(pip, '21.0')\n",
+    "check_version(numpy, '1.19')\n",
+    "check_version(matplotlib, '3.3')\n",
+    "check_version(sklearn, '0.24')\n",
+    "check_version(scipy, '1.6')\n",
+    "check_version(pandas, '1.2')\n",
+    "check_version(PIL, '8.2')\n",
+    "check_version(seaborn, '0.11')\n",
+    "check_version(tensorflow, '2.5')\n",
+    "\n",
+    "print(\"Houston we are go!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/1 First Deep Learning Model-Copy1.ipynb b/course/1 First Deep Learning Model-Copy1.ipynb
new file mode 100644
index 0000000..48f8976
--- /dev/null
+++ b/course/1 First Deep Learning Model-Copy1.ipynb	
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Deep Learning Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # import the numpy library and assign the name np to it\n",
+    "%matplotlib inline # magic function that sets the backend of matplotlib to the inline backend\n",
+    "import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_circles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_circles(n_samples=1000,\n",
+    "                    noise=0.1,\n",
+    "                    factor=0.2,\n",
+    "                    random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(4, input_shape=(2,), activation='tanh'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X, y, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hticks = np.linspace(-1.5, 1.5, 101)\n",
+    "vticks = np.linspace(-1.5, 1.5, 101)\n",
+    "aa, bb = np.meshgrid(hticks, vticks)\n",
+    "ab = np.c_[aa.ravel(), bb.ravel()]\n",
+    "c = model.predict(ab)\n",
+    "cc = c.reshape(aa.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/1 First Deep Learning Model.ipynb b/course/1 First Deep Learning Model.ipynb
new file mode 100644
index 0000000..d4563b8
--- /dev/null
+++ b/course/1 First Deep Learning Model.ipynb	
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Deep Learning Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_circles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_circles(n_samples=1000,\n",
+    "                    noise=0.1,\n",
+    "                    factor=0.2,\n",
+    "                    random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(4, input_shape=(2,), activation='tanh'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X, y, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hticks = np.linspace(-1.5, 1.5, 101)\n",
+    "vticks = np.linspace(-1.5, 1.5, 101)\n",
+    "aa, bb = np.meshgrid(hticks, vticks)\n",
+    "ab = np.c_[aa.ravel(), bb.ravel()]\n",
+    "c = model.predict(ab)\n",
+    "cc = c.reshape(aa.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/2 Data.ipynb b/course/2 Data.ipynb
new file mode 100644
index 0000000..a8eec83
--- /dev/null
+++ b/course/2 Data.ipynb	
@@ -0,0 +1,1892 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Exploration with Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/titanic-train.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.iloc[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[0:4,'Ticket']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Ticket'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[['Embarked', 'Ticket']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df['Age'] > 70]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'] > 70"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.query(\"Age > 70\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[(df['Age'] == 11) & (df['SibSp'] == 5)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[(df.Age == 11) | (df.SibSp == 5)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.query('(Age == 11) | (SibSp == 5)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unique Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['S', 'C', 'Q', nan], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Embarked'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sorting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.sort_values('Age', ascending = False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Survived'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Pclass'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.groupby(['Pclass', 'Survived'])['PassengerId'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_age_by_survived = df.groupby('Survived')['Age'].mean()\n",
+    "mean_age_by_survived"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std_age_by_survived = df.groupby('Survived')['Age'].std()\n",
+    "std_age_by_survived"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'mean_age_by_survived' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-8-edbb1563a439>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmean_age_by_survived\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstd_age_by_survived\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'mean_age_by_survived' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df1 = mean_age_by_survived.round(0).reset_index()\n",
+    "df2 = std_age_by_survived.round(0).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-6a861a8b3f1a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'df1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df2' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-2068723f1203>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'df2' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-764a1093041e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Survived'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'df1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df3 = pd.merge(df1, df2, on='Survived')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3.columns = ['Survived', 'Average Age', 'Age Standard Deviation']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pivot Tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Survived</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>80</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>97</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>372</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Survived    0    1\n",
+       "Pclass            \n",
+       "1          80  136\n",
+       "2          97   87\n",
+       "3         372  119"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.pivot_table(index='Pclass',\n",
+    "               columns='Survived',\n",
+    "               values='PassengerId',\n",
+    "               aggfunc='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['IsFemale'] = df['Sex'] == 'female'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correlated_with_survived = df.corr()['Survived'].sort_values()\n",
+    "correlated_with_survived"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correlated_with_survived.iloc[:-1].plot(kind='bar',\n",
+    "                                        title='Titanic Passengers: correlation with survival');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visual Data Exploration with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = np.random.normal(0, 0.1, 1000)\n",
+    "data2 = np.random.normal(1, 0.4, 1000) + np.linspace(0, 1, 1000)\n",
+    "data3 = 2 + np.random.random(1000) * np.linspace(1, 5, 1000)\n",
+    "data4 = np.random.normal(3, 0.2, 1000) + 0.3 * np.sin(np.linspace(0, 20, 1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.vstack([data1, data2, data3, data4]).transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data1</th>\n",
+       "      <th>data2</th>\n",
+       "      <th>data3</th>\n",
+       "      <th>data4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.156565</td>\n",
+       "      <td>1.055747</td>\n",
+       "      <td>2.209177</td>\n",
+       "      <td>2.990694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.061588</td>\n",
+       "      <td>1.061770</td>\n",
+       "      <td>2.282282</td>\n",
+       "      <td>3.065291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.074836</td>\n",
+       "      <td>0.427742</td>\n",
+       "      <td>2.932798</td>\n",
+       "      <td>3.394612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.014828</td>\n",
+       "      <td>1.061186</td>\n",
+       "      <td>2.994922</td>\n",
+       "      <td>2.871135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.081135</td>\n",
+       "      <td>0.937794</td>\n",
+       "      <td>2.062173</td>\n",
+       "      <td>3.322928</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      data1     data2     data3     data4\n",
+       "0  0.156565  1.055747  2.209177  2.990694\n",
+       "1 -0.061588  1.061770  2.282282  3.065291\n",
+       "2 -0.074836  0.427742  2.932798  3.394612\n",
+       "3  0.014828  1.061186  2.994922  2.871135\n",
+       "4 -0.081135  0.937794  2.062173  3.322928"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data, columns=['data1', 'data2', 'data3', 'data4'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Line Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(title='Line plot');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df)\n",
+    "plt.title('Line plot')\n",
+    "plt.legend(['data1', 'data2', 'data3', 'data4']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scatter Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(style='.');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = df.plot(kind='scatter', x='data1', y='data2',\n",
+    "            xlim=(-1.5, 1.5), ylim=(0, 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='hist',\n",
+    "        bins=50,\n",
+    "        title='Histogram',\n",
+    "        alpha=0.6);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cumulative distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='hist',\n",
+    "        bins=100,\n",
+    "        title='Cumulative distributions',\n",
+    "        density=True,\n",
+    "        cumulative=True,\n",
+    "        alpha=0.4);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Box Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='box',\n",
+    "        title='Boxplot');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Subplots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(5, 5))\n",
+    "\n",
+    "df.plot(ax=ax[0][0],\n",
+    "        title='Line plot')\n",
+    "\n",
+    "df.plot(ax=ax[0][1],\n",
+    "        style='o',\n",
+    "        title='Scatter plot')\n",
+    "\n",
+    "df.plot(ax=ax[1][0],\n",
+    "        kind='hist',\n",
+    "        bins=50,\n",
+    "        title='Histogram')\n",
+    "\n",
+    "df.plot(ax=ax[1][1],\n",
+    "        kind='box',\n",
+    "        title='Boxplot')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pie charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    837\n",
+       "True     163\n",
+       "Name: data1, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt01 = df['data1'] > 0.1\n",
+    "piecounts = gt01.value_counts()\n",
+    "piecounts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "piecounts.plot(kind='pie',\n",
+    "               figsize=(5, 5),\n",
+    "               explode=[0, 0.15],\n",
+    "               labels=['<= 0.1', '> 0.1'],\n",
+    "               autopct='%1.1f%%',\n",
+    "               shadow=True,\n",
+    "               startangle=90,\n",
+    "               fontsize=16);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hexbin plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.vstack([np.random.normal((0, 0), 2, size=(1000, 2)),\n",
+    "                  np.random.normal((9, 9), 3, size=(2000, 2))])\n",
+    "df = pd.DataFrame(data, columns=['x', 'y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.471902</td>\n",
+       "      <td>-4.371674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.161619</td>\n",
+       "      <td>-1.511347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.369703</td>\n",
+       "      <td>0.065523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.969280</td>\n",
+       "      <td>0.401674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.307002</td>\n",
+       "      <td>-2.472527</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          x         y\n",
+       "0 -1.471902 -4.371674\n",
+       "1  0.161619 -1.511347\n",
+       "2  2.369703  0.065523\n",
+       "3 -0.969280  0.401674\n",
+       "4  0.307002 -2.472527"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='kde');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='hexbin', x='x', y='y', bins=100, cmap='rainbow');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unstructured data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x435 at 0x1C516ACEC48>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = Image.open('../data/iss.jpg')\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PIL.JpegImagePlugin.JpegImageFile"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imgarray = np.asarray(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(imgarray)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(435, 640, 3)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imgarray.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(835200,)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imgarray.ravel().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "835200"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "435 * 640 * 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sound"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.io import wavfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rate, snd = wavfile.read(filename='../data/sms.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Audio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Audio(data=snd, rate=rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "110250"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(snd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([70, 14, 27, ..., 58, 68, 59], dtype=int16)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1c51888d3c8>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(snd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Time (s)')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plt.specgram(snd, NFFT=1024, Fs=44100)\n",
+    "plt.ylabel('Frequency (Hz)')\n",
+    "plt.xlabel('Time (s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Exploration Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "- load the dataset: `../data/international-airline-passengers.csv`\n",
+    "- inspect it using the `.info()` and `.head()` commands\n",
+    "- use the function [`pd.to_datetime()`](http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.to_datetime.html) to change the column type of 'Month' to a datatime type\n",
+    "- set the index of df to be a datetime index using the column 'Month' and the `df.set_index()` method\n",
+    "- choose the appropriate plot and display the data\n",
+    "- choose appropriate scale\n",
+    "- label the axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "- load the dataset: `../data/weight-height.csv`\n",
+    "- inspect it\n",
+    "- plot it using a scatter plot with Weight as a function of Height\n",
+    "- plot the male and female populations with 2 different colors on a new scatter plot\n",
+    "- remember to label the axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "- plot the histogram of the heights for males and for females on the same plot\n",
+    "- use alpha to control transparency in the plot comand\n",
+    "- plot a vertical line at the mean of each population using `plt.axvline()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "- plot the weights of the males and females using a box plot\n",
+    "- which one is easier to read?\n",
+    "- (remember to put in titles, axes and legends)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5\n",
+    "- load the dataset: `../data/titanic-train.csv`\n",
+    "- learn about scattermatrix here: http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
+    "- display the data using a scattermatrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/3 Machine Learning.ipynb b/course/3 Machine Learning.ipynb
new file mode 100644
index 0000000..54b455b
--- /dev/null
+++ b/course/3 Machine Learning.ipynb	
@@ -0,0 +1,1177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/weight-height.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>73.847017</td>\n",
+       "      <td>241.893563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>68.781904</td>\n",
+       "      <td>162.310473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>74.110105</td>\n",
+       "      <td>212.740856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>71.730978</td>\n",
+       "      <td>220.042470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>69.881796</td>\n",
+       "      <td>206.349801</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Gender     Height      Weight\n",
+       "0   Male  73.847017  241.893563\n",
+       "1   Male  68.781904  162.310473\n",
+       "2   Male  74.110105  212.740856\n",
+       "3   Male  71.730978  220.042470\n",
+       "4   Male  69.881796  206.349801"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Weight and Height in adults'}, xlabel='Height', ylabel='Weight'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='scatter',\n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        title='Weight and Height in adults')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind='scatter',\n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        title='Weight and Height in adults')\n",
+    "\n",
+    "# Here we're plotting the red line 'by hand' with fixed values\n",
+    "# We'll try to learn this line with an algorithm below\n",
+    "plt.plot([55, 78], [75, 250], color='red', linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def line(x, w=0, b=0):\n",
+    "    return x * w + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(55, 80, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = line(x, w=0, b=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind='scatter',\n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        title='Weight and Height in adults')\n",
+    "plt.plot(x, yhat, color='red', linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cost Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_squared_error(y_true, y_pred):\n",
+    "    s = (y_true - y_pred)**2\n",
+    "    return s.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[['Height']].values\n",
+    "y_true = df['Weight'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = line(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_squared_error(y_true, y_pred.ravel())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### you do it!\n",
+    "\n",
+    "Try changing the values of the parameters b and w in the line above and plot it again to see how the plot and the cost  change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# we are going to draw 2 plots in the same figure\n",
+    "# first plot, data and a few lines\n",
+    "ax1 = plt.subplot(121)\n",
+    "df.plot(kind='scatter',\n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        title='Weight and Height in adults', ax=ax1)\n",
+    "\n",
+    "# let's explore the cost function for a few values of b between -100 and +150\n",
+    "bbs = np.array([-100, -50, 0, 50, 100, 150])\n",
+    "mses = []  # we will append the values of the cost here, for each line\n",
+    "for b in bbs:\n",
+    "    y_pred = line(X, w=2, b=b)\n",
+    "    mse = mean_squared_error(y_true, y_pred)\n",
+    "    mses.append(mse)\n",
+    "    plt.plot(X, y_pred)\n",
+    "\n",
+    "# second plot: Cost function\n",
+    "ax2 = plt.subplot(122)\n",
+    "plt.plot(bbs, mses, 'o-')\n",
+    "plt.title('Cost as a function of b')\n",
+    "plt.xlabel('b');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Regression with Keras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam, SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(1, input_shape=(1,)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(Adam(learning_rate=0.8), 'mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X, y_true, epochs=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind='scatter',\n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        title='Weight and Height in adults')\n",
+    "plt.plot(X, y_pred, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W, B = model.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating Model Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The R2 score is {:0.3f}\".format(r2_score(y_true, y_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_true,\n",
+    "                                                    test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W[0, 0] = 0.0\n",
+    "B[0] = 0.0\n",
+    "model.set_weights((W, B))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=50, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_pred = model.predict(X_train).ravel()\n",
+    "y_test_pred = model.predict(X_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error as mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The Mean Squared Error on the Train set is:\\t{:0.1f}\".format(mse(y_train, y_train_pred)))\n",
+    "print(\"The Mean Squared Error on the Test set is:\\t{:0.1f}\".format(mse(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
+    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/user_visit_duration.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind='scatter', x='Time (min)', y='Buy');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_shape=(1,), activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[['Time (min)']].values\n",
+    "y = df['Buy'].values\n",
+    "\n",
+    "model.fit(X, y, epochs=25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = df.plot(kind='scatter', x='Time (min)', y='Buy',\n",
+    "             title='Purchase behavior VS time spent on site')\n",
+    "\n",
+    "temp = np.linspace(0, 4)\n",
+    "ax.plot(temp, model.predict(temp), color='orange')\n",
+    "plt.legend(['model', 'data'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_class = model.predict(temp) > 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = df.plot(kind='scatter', x='Time (min)', y='Buy',\n",
+    "             title='Purchase behavior VS time spent on site')\n",
+    "\n",
+    "temp = np.linspace(0, 4)\n",
+    "ax.plot(temp, temp_class, color='orange')\n",
+    "plt.legend(['model', 'data'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X)\n",
+    "y_class_pred = y_pred > 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The accuracy score is {:0.3f}\".format(accuracy_score(y, y_class_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train/Test split\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = model.get_weights()\n",
+    "params = [np.zeros(w.shape) for w in params]\n",
+    "model.set_weights(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The accuracy score is {:0.3f}\".format(accuracy_score(y, model.predict(X) > 0.5)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=25, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The train accuracy score is {:0.3f}\".format(accuracy_score(y_train, model.predict(X_train) > 0.5)))\n",
+    "print(\"The test accuracy score is {:0.3f}\".format(accuracy_score(y_test, model.predict(X_test) > 0.5)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_logistic_regression_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_shape=(1,), activation='sigmoid'))\n",
+    "    model.compile(SGD(learning_rate=0.5),\n",
+    "                  'binary_crossentropy',\n",
+    "                  metrics=['accuracy'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KerasClassifier(build_fn=build_logistic_regression_model,\n",
+    "                        epochs=25,\n",
+    "                        verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score, KFold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = KFold(3, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = cross_val_score(model, X, y, cv=cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The cross validation accuracy is {:0.4f} ± {:0.4f}\".format(scores.mean(), scores.std()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "confusion_matrix(y, y_class_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pretty_confusion_matrix(y_true, y_pred, labels=[\"False\", \"True\"]):\n",
+    "    cm = confusion_matrix(y_true, y_pred)\n",
+    "    pred_labels = ['Predicted '+ l for l in labels]\n",
+    "    df = pd.DataFrame(cm, index=labels, columns=pred_labels)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pretty_confusion_matrix(y, y_class_pred, ['Not Buy', 'Buy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_score, recall_score, f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Precision:\\t{:0.3f}\".format(precision_score(y, y_class_pred)))\n",
+    "print(\"Recall:  \\t{:0.3f}\".format(recall_score(y, y_class_pred)))\n",
+    "print(\"F1 Score:\\t{:0.3f}\".format(f1_score(y, y_class_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(y, y_class_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Categorical Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/weight-height.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gender'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.get_dummies(df['Gender'], prefix='Gender').head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Transformations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1) Rescale with fixed factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Height (feet)'] = df['Height']/12.0\n",
+    "df['Weight (100 lbs)'] = df['Weight']/100.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MinMax normalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "mms = MinMaxScaler()\n",
+    "df['Weight_mms'] = mms.fit_transform(df[['Weight']])\n",
+    "df['Height_mms'] = mms.fit_transform(df[['Height']])\n",
+    "df.describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3) Standard normalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "ss = StandardScaler()\n",
+    "df['Weight_ss'] = ss.fit_transform(df[['Weight']])\n",
+    "df['Height_ss'] = ss.fit_transform(df[['Height']])\n",
+    "df.describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "for i, feature in enumerate(['Height', 'Height (feet)', 'Height_mms', 'Height_ss']):\n",
+    "    plt.subplot(1, 4, i+1)\n",
+    "    df[feature].plot(kind='hist', title=feature)\n",
+    "    plt.xlabel(feature);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "You've just been hired at a real estate investment firm and they would like you to build a model for pricing houses. You are given a dataset that contains data for house prices and a few features like number of bedrooms, size in square feet and age of the house. Let's see if you can build a model that is able to predict the price. In this exercise we extend what we have learned about linear regression to a dataset with more than one feature. Here are the steps to complete it:\n",
+    "\n",
+    "1. Load the dataset ../data/housing-data.csv\n",
+    "- plot the histograms for each feature\n",
+    "- create 2 variables called X and y: X shall be a matrix with 3 columns (sqft,bdrms,age) and y shall be a vector with 1 column (price)\n",
+    "- create a linear regression model in Keras with the appropriate number of inputs and output\n",
+    "- split the data into train and test with a 20% test size\n",
+    "- train the model on the training set and check its accuracy on training and test set\n",
+    "- how's your model doing? Is the loss growing smaller?\n",
+    "- try to improve your model with these experiments:\n",
+    "    - normalize the input features with one of the rescaling techniques mentioned above\n",
+    "    - use a different value for the learning rate of your model\n",
+    "    - use a different optimizer\n",
+    "- once you're satisfied with training, check the R2score on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "Your boss was extremely happy with your work on the housing price prediction model and decided to entrust you with a more challenging task. They've seen a lot of people leave the company recently and they would like to understand why that's happening. They have collected historical data on employees and they would like you to build a model that is able to predict which employee will leave next. They would like a model that is better than random guessing. They also prefer false negatives than false positives, in this first phase. Fields in the dataset include:\n",
+    "\n",
+    "- Employee satisfaction level\n",
+    "- Last evaluation\n",
+    "- Number of projects\n",
+    "- Average monthly hours\n",
+    "- Time spent at the company\n",
+    "- Whether they have had a work accident\n",
+    "- Whether they have had a promotion in the last 5 years\n",
+    "- Department\n",
+    "- Salary\n",
+    "- Whether the employee has left\n",
+    "\n",
+    "Your goal is to predict the binary outcome variable `left` using the rest of the data. Since the outcome is binary, this is a classification problem. Here are some things you may want to try out:\n",
+    "\n",
+    "1. load the dataset at ../data/HR_comma_sep.csv, inspect it with `.head()`, `.info()` and `.describe()`.\n",
+    "- Establish a benchmark: what would be your accuracy score if you predicted everyone stay?\n",
+    "- Check if any feature needs rescaling. You may plot a histogram of the feature to decide which rescaling method is more appropriate.\n",
+    "- convert the categorical features into binary dummy columns. You will then have to combine them with the numerical features using `pd.concat`.\n",
+    "- do the usual train/test split with a 20% test size\n",
+    "- play around with learning rate and optimizer\n",
+    "- check the confusion matrix, precision and recall\n",
+    "- check if you still get the same results if you use a 5-Fold cross validation on all the data\n",
+    "- Is the model good enough for your boss?\n",
+    "\n",
+    "As you will see in this exercise, the a logistic regression model is not good enough to help your boss. In the next chapter we will learn how to go beyond linear models.\n",
+    "\n",
+    "This dataset comes from https://www.kaggle.com/ludobenistant/hr-analytics/ and is released under [CC BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/4 Deep Learning Intro.ipynb b/course/4 Deep Learning Intro.ipynb
new file mode 100644
index 0000000..a047d0c
--- /dev/null
+++ b/course/4 Deep Learning Intro.ipynb	
@@ -0,0 +1,560 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Learning Intro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shallow and Deep Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "\n",
+    "X, y = make_moons(n_samples=1000, noise=0.1, random_state=0)\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.legend(['0', '1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
+    "                                                    test_size=0.3,\n",
+    "                                                    random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD, Adam"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shallow Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_shape=(2,), activation='sigmoid'))\n",
+    "model.compile(Adam(learning_rate=0.05), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=200, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = model.evaluate(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The Accuracy score on the Train set is:\\t{:0.3f}\".format(results[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_decision_boundary(model, X, y):\n",
+    "    amin, bmin = X.min(axis=0) - 0.1\n",
+    "    amax, bmax = X.max(axis=0) + 0.1\n",
+    "    hticks = np.linspace(amin, amax, 101)\n",
+    "    vticks = np.linspace(bmin, bmax, 101)\n",
+    "    \n",
+    "    aa, bb = np.meshgrid(hticks, vticks)\n",
+    "    ab = np.c_[aa.ravel(), bb.ravel()]\n",
+    "    \n",
+    "    c = model.predict(ab)\n",
+    "    cc = c.reshape(aa.shape)\n",
+    "\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
+    "    plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "    plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "    plt.legend(['0', '1'])\n",
+    "    \n",
+    "plot_decision_boundary(model, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deep model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(4, input_shape=(2,), activation='tanh'))\n",
+    "model.add(Dense(2, activation='tanh'))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "model.compile(Adam(learning_rate=0.05), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=100, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.evaluate(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_pred = model.predict_classes(X_train)\n",
+    "y_test_pred = model.predict_classes(X_test)\n",
+    "\n",
+    "print(\"The Accuracy score on the Train set is:\\t{:0.3f}\".format(accuracy_score(y_train, y_train_pred)))\n",
+    "print(\"The Accuracy score on the Test set is:\\t{:0.3f}\".format(accuracy_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_decision_boundary(model, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiclass classification\n",
+    "\n",
+    "### The Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.pairplot(df, hue=\"species\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop('species', axis=1)\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_names = df['species'].unique()\n",
+    "target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_dict = {n:i for i, n in enumerate(target_names)}\n",
+    "target_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y= df['species'].map(target_dict)\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X.values, y_cat,\n",
+    "                                                    test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(3, input_shape=(4,), activation='softmax'))\n",
+    "model.compile(Adam(learning_rate=0.1),\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_class = np.argmax(y_test, axis=1)\n",
+    "y_pred_class = np.argmax(y_pred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(y_test_class, y_pred_class))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "confusion_matrix(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The [Pima Indians dataset](https://archive.ics.uci.edu/ml/datasets/diabetes) is a very famous dataset distributed by UCI and originally collected from the National Institute of Diabetes and Digestive and Kidney Diseases. It contains data from clinical exams for women age 21 and above of Pima indian origins. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n",
+    "\n",
+    "It has the following features:\n",
+    "\n",
+    "- Pregnancies: Number of times pregnant\n",
+    "- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
+    "- BloodPressure: Diastolic blood pressure (mm Hg)\n",
+    "- SkinThickness: Triceps skin fold thickness (mm)\n",
+    "- Insulin: 2-Hour serum insulin (mu U/ml)\n",
+    "- BMI: Body mass index (weight in kg/(height in m)^2)\n",
+    "- DiabetesPedigreeFunction: Diabetes pedigree function\n",
+    "- Age: Age (years)\n",
+    "\n",
+    "The last colum is the outcome, and it is a binary variable.\n",
+    "\n",
+    "In this first exercise we will explore it through the following steps:\n",
+    "\n",
+    "1. Load the ..data/diabetes.csv dataset, use pandas to explore the range of each feature\n",
+    "- For each feature draw a histogram. Bonus points if you draw all the histograms in the same figure.\n",
+    "- Explore correlations of features with the outcome column. You can do this in several ways, for example using the `sns.pairplot` we used above or drawing a heatmap of the correlations.\n",
+    "- Do features need standardization? If so what stardardization technique will you use? MinMax? Standard?\n",
+    "- Prepare your final `X` and `y` variables to be used by a ML model. Make sure you define your target variable well. Will you need dummy columns?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Build a fully connected NN model that predicts diabetes. Follow these steps:\n",
+    "\n",
+    "1. Split your data in a train/test with a test size of 20% and a `random_state = 22`\n",
+    "- define a sequential model with at least one inner layer. You will have to make choices for the following things:\n",
+    "    - what is the size of the input?\n",
+    "    - how many nodes will you use in each layer?\n",
+    "    - what is the size of the output?\n",
+    "    - what activation functions will you use in the inner layers?\n",
+    "    - what activation function will you use at output?\n",
+    "    - what loss function will you use?\n",
+    "    - what optimizer will you use?\n",
+    "- fit your model on the training set, using a validation_split of 0.1\n",
+    "- test your trained model on the test data from the train/test split\n",
+    "- check the accuracy score, the confusion matrix and the classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "Compare your work with the results presented in [this notebook](https://www.kaggle.com/futurist/d/uciml/pima-indians-diabetes-database/pima-data-visualisation-and-machine-learning). Are your Neural Network results better or worse than the results obtained by traditional Machine Learning techniques?\n",
+    "\n",
+    "- Try training a Support Vector Machine or a Random Forest model on the exact same train/test split. Is the performance better or worse?\n",
+    "- Try restricting your features to only 4 features like in the suggested notebook. How does model performance change?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "\n",
+    "[Tensorflow playground](http://playground.tensorflow.org/) is a web based neural network demo. It is really useful to develop an intuition about what happens when you change architecture, activation function or other parameters. Try playing with it for a few minutes. You don't need do understand the meaning of every knob and button in the page, just get a sense for what happens if you change something. In the next chapter we'll explore these things in more detail.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/5 Gradient Descent.ipynb b/course/5 Gradient Descent.ipynb
new file mode 100644
index 0000000..b77dfc9
--- /dev/null
+++ b/course/5 Gradient Descent.ipynb	
@@ -0,0 +1,1055 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Algebra with Numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1, 3, 2, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[3, 1, 2],\n",
+    "              [2, 3, 4]])\n",
+    "\n",
+    "B = np.array([[0, 1],\n",
+    "              [2, 3],\n",
+    "              [4, 5]])\n",
+    "\n",
+    "C = np.array([[0, 1],\n",
+    "              [2, 3],\n",
+    "              [4, 5],\n",
+    "              [0, 1],\n",
+    "              [2, 3],\n",
+    "              [4, 5]])\n",
+    "\n",
+    "print(\"A is a {} matrix\".format(A.shape))\n",
+    "print(\"B is a {} matrix\".format(B.shape))\n",
+    "print(\"C is a {} matrix\".format(C.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C[2, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Elementwise operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "3 * A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A + A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A * A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A / A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A - A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uncomment the code in the next cells. You will see that tensors of different shape cannot be added or multiplied:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A + B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A * B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dot product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.dot(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.dot(A, B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B.dot(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C.dot(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uncomment the code in the next cell to visualize the error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A.dot(C)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradient descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../data/banknotes.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/banknotes.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['class'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(df, hue=\"class\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Baseline model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.preprocessing import scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = scale(df.drop('class', axis=1).values)\n",
+    "y = df['class'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestClassifier()\n",
+    "cross_val_score(model, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic Regression Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
+    "                                                    test_size=0.3,\n",
+    "                                                    random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow.keras.backend as K\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Activation\n",
+    "from tensorflow.keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_shape=(4,), activation='sigmoid'))\n",
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "history = model.fit(X_train, y_train, epochs=10)\n",
+    "result = model.evaluate(X_test, y_test, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf = pd.DataFrame(history.history, index=history.epoch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf.plot(ylim=(0,1))\n",
+    "plt.title(\"Test accuracy: {:3.1f} %\".format(result[1]*100), fontsize=15);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning Rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflist = []\n",
+    "\n",
+    "learning_rates = [0.01, 0.05, 0.1, 0.5]\n",
+    "\n",
+    "for lr in learning_rates:\n",
+    "\n",
+    "    K.clear_session()\n",
+    "\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_shape=(4,), activation='sigmoid'))\n",
+    "    model.compile(loss='binary_crossentropy',\n",
+    "                  optimizer=SGD(learning_rate=lr),\n",
+    "                  metrics=['accuracy'])\n",
+    "    h = model.fit(X_train, y_train, batch_size=16, epochs=10, verbose=0)\n",
+    "    \n",
+    "    dflist.append(pd.DataFrame(h.history, index=h.epoch))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf = pd.concat(dflist, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics_reported = dflist[0].columns\n",
+    "idx = pd.MultiIndex.from_product([learning_rates, metrics_reported],\n",
+    "                                 names=['learning_rate', 'metric'])\n",
+    "\n",
+    "historydf.columns = idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "ax = plt.subplot(211)\n",
+    "historydf.xs('loss', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Loss\")\n",
+    "\n",
+    "ax = plt.subplot(212)\n",
+    "historydf.xs('accuracy', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Accuracy\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Batch Sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflist = []\n",
+    "\n",
+    "batch_sizes = [16, 32, 64, 128]\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    K.clear_session()\n",
+    "\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_shape=(4,), activation='sigmoid'))\n",
+    "    model.compile(loss='binary_crossentropy',\n",
+    "                  optimizer='sgd',\n",
+    "                  metrics=['accuracy'])\n",
+    "    h = model.fit(X_train, y_train, batch_size=batch_size, epochs=10, verbose=0)\n",
+    "    \n",
+    "    dflist.append(pd.DataFrame(h.history, index=h.epoch))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf = pd.concat(dflist, axis=1)\n",
+    "metrics_reported = dflist[0].columns\n",
+    "idx = pd.MultiIndex.from_product([batch_sizes, metrics_reported],\n",
+    "                                 names=['batch_size', 'metric'])\n",
+    "historydf.columns = idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "ax = plt.subplot(211)\n",
+    "historydf.xs('loss', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Loss\")\n",
+    "\n",
+    "ax = plt.subplot(212)\n",
+    "historydf.xs('accuracy', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Accuracy\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.optimizers import SGD, Adam, Adagrad, RMSprop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflist = []\n",
+    "\n",
+    "optimizers = ['SGD(learning_rate=0.01)',\n",
+    "              'SGD(learning_rate=0.01, momentum=0.3)',\n",
+    "              'SGD(learning_rate=0.01, momentum=0.3, nesterov=True)',  \n",
+    "              'Adam(learning_rate=0.01)',\n",
+    "              'Adagrad(learning_rate=0.01)',\n",
+    "              'RMSprop(learning_rate=0.01)']\n",
+    "\n",
+    "for opt_name in optimizers:\n",
+    "\n",
+    "    K.clear_session()\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_shape=(4,), activation='sigmoid'))\n",
+    "    model.compile(loss='binary_crossentropy',\n",
+    "                  optimizer=eval(opt_name),\n",
+    "                  metrics=['accuracy'])\n",
+    "    h = model.fit(X_train, y_train, batch_size=16, epochs=5, verbose=0)\n",
+    "    \n",
+    "    dflist.append(pd.DataFrame(h.history, index=h.epoch))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf = pd.concat(dflist, axis=1)\n",
+    "metrics_reported = dflist[0].columns\n",
+    "idx = pd.MultiIndex.from_product([optimizers, metrics_reported],\n",
+    "                                 names=['optimizers', 'metric'])\n",
+    "historydf.columns = idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "ax = plt.subplot(211)\n",
+    "historydf.xs('loss', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Loss\")\n",
+    "\n",
+    "ax = plt.subplot(212)\n",
+    "historydf.xs('accuracy', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Accuracy\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "https://keras.io/initializers/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflist = []\n",
+    "\n",
+    "initializers = ['zeros', 'uniform', 'normal',\n",
+    "                'he_normal', 'lecun_uniform']\n",
+    "\n",
+    "for init in initializers:\n",
+    "\n",
+    "    K.clear_session()\n",
+    "\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_shape=(4,),\n",
+    "                    kernel_initializer=init,\n",
+    "                    activation='sigmoid'))\n",
+    "\n",
+    "    model.compile(loss='binary_crossentropy',\n",
+    "                  optimizer='rmsprop',\n",
+    "                  metrics=['accuracy'])\n",
+    "\n",
+    "    h = model.fit(X_train, y_train, batch_size=16, epochs=5, verbose=0)\n",
+    "    \n",
+    "    dflist.append(pd.DataFrame(h.history, index=h.epoch))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "historydf = pd.concat(dflist, axis=1)\n",
+    "metrics_reported = dflist[0].columns\n",
+    "idx = pd.MultiIndex.from_product([initializers, metrics_reported],\n",
+    "                                 names=['initializers', 'metric'])\n",
+    "\n",
+    "historydf.columns = idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "ax = plt.subplot(211)\n",
+    "historydf.xs('loss', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Loss\")\n",
+    "\n",
+    "ax = plt.subplot(212)\n",
+    "historydf.xs('accuracy', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
+    "plt.title(\"Accuracy\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inner layer representation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(2, input_shape=(4,), activation='relu'))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=RMSprop(learning_rate=0.01),\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "h = model.fit(X_train, y_train, batch_size=16, epochs=20,\n",
+    "              verbose=1, validation_split=0.3)\n",
+    "result = model.evaluate(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inp = model.layers[0].input\n",
+    "out = model.layers[0].output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_function = K.function([inp], [out])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_function([X_test])[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = features_function([X_test])[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(features[:, 0], features[:, 1], c=y_test, cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(3, input_shape=(4,), activation='relu'))\n",
+    "model.add(Dense(2, activation='relu'))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=RMSprop(learning_rate=0.01),\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inp = model.layers[0].input\n",
+    "out = model.layers[1].output\n",
+    "features_function = K.function([inp], [out])\n",
+    "\n",
+    "plt.figure(figsize=(15,10))\n",
+    "\n",
+    "for i in range(1, 26):\n",
+    "    plt.subplot(5, 5, i)\n",
+    "    h = model.fit(X_train, y_train, batch_size=16, epochs=1, verbose=0)\n",
+    "    test_accuracy = model.evaluate(X_test, y_test, verbose=0)[1]\n",
+    "    features = features_function([X_test])[0]\n",
+    "    plt.scatter(features[:, 0], features[:, 1], c=y_test, cmap='coolwarm')\n",
+    "    plt.xlim(-0.5, 3.5)\n",
+    "    plt.ylim(-0.5, 4.0)\n",
+    "    plt.title('Epoch: {}, Test Acc: {:3.1f} %'.format(i, test_accuracy * 100.0))\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 1\n",
+    "\n",
+    "You've just been hired at a wine company and they would like you to help them build a model that predicts the quality of their wine based on several measurements. They give you a dataset with wine\n",
+    "\n",
+    "- Load the ../data/wines.csv into Pandas\n",
+    "- Use the column called \"Class\" as target\n",
+    "- Check how many classes are there in target, and if necessary use dummy columns for a multi-class classification\n",
+    "- Use all the other columns as features, check their range and distribution (using seaborn pairplot)\n",
+    "- Rescale all the features using either MinMaxScaler or StandardScaler\n",
+    "- Build a deep model with at least 1 hidden layer to classify the data\n",
+    "- Choose the cost function, what will you use? Mean Squared Error? Binary Cross-Entropy? Categorical Cross-Entropy?\n",
+    "- Choose an optimizer\n",
+    "- Choose a value for the learning rate, you may want to try with several values\n",
+    "- Choose a batch size\n",
+    "- Train your model on all the data using a `validation_split=0.2`. Can you converge to 100% validation accuracy?\n",
+    "- What's the minumum number of epochs to converge?\n",
+    "- Repeat the training several times to verify how stable your results are"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 2\n",
+    "\n",
+    "Since this dataset has 13 features we can only visualize pairs of features like we did in the Paired plot. We could however exploit the fact that a neural network is a function to extract 2 high level features to represent our data.\n",
+    "\n",
+    "- Build a deep fully connected network with the following structure:\n",
+    "    - Layer 1: 8 nodes\n",
+    "    - Layer 2: 5 nodes\n",
+    "    - Layer 3: 2 nodes\n",
+    "    - Output : 3 nodes\n",
+    "- Choose activation functions, inizializations, optimizer and learning rate so that it converges to 100% accuracy within 20 epochs (not easy)\n",
+    "- Remember to train the model on the scaled data\n",
+    "- Define a Feature Funtion like we did above between the input of the 1st layer and the output of the 3rd layer\n",
+    "- Calculate the features and plot them on a 2-dimensional scatter plot\n",
+    "- Can we distinguish the 3 classes well?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 3\n",
+    "\n",
+    "Keras functional API. So far we've always used the Sequential model API in Keras. However, Keras also offers a Functional API, which is much more powerful. You can find its [documentation here](https://keras.io/getting-started/functional-api-guide/). Let's see how we can leverage it.\n",
+    "\n",
+    "- define an input layer called `inputs`\n",
+    "- define two hidden layers as before, one with 8 nodes, one with 5 nodes\n",
+    "- define a `second_to_last` layer with 2 nodes\n",
+    "- define an output layer with 3 nodes\n",
+    "- create a model that connect input and output\n",
+    "- train it and make sure that it converges\n",
+    "- define a function between inputs and second_to_last layer\n",
+    "- recalculate the features and plot them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 4 \n",
+    "\n",
+    "Keras offers the possibility to call a function at each epoch. These are Callbacks, and their [documentation is here](https://keras.io/callbacks/). Callbacks allow us to add some neat functionality. In this exercise we'll explore a few of them.\n",
+    "\n",
+    "- Split the data into train and test sets with a test_size = 0.3 and random_state=42\n",
+    "- Reset and recompile your model\n",
+    "- train the model on the train data using `validation_data=(X_test, y_test)`\n",
+    "- Use the `EarlyStopping` callback to stop your training if the `val_loss` doesn't improve\n",
+    "- Use the `ModelCheckpoint` callback to save the trained model to disk once training is finished\n",
+    "- Use the `TensorBoard` callback to output your training information to a `/tmp/` subdirectory\n",
+    "- Watch the next video for an overview of tensorboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/6 Convolutional Neural Networks.ipynb b/course/6 Convolutional Neural Networks.ipynb
new file mode 100644
index 0000000..cdc79a4
--- /dev/null
+++ b/course/6 Convolutional Neural Networks.ipynb	
@@ -0,0 +1,988 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Machine learning on images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data('/tmp/mnist.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(X_train[0], cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.reshape(-1, 28*28)\n",
+    "X_test = X_test.reshape(-1, 28*28)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "X_train /= 255.0\n",
+    "X_test /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_cat = to_categorical(y_train)\n",
+    "y_test_cat = to_categorical(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_cat[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_cat.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_cat.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fully connected on images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "import tensorflow.keras.backend as K\n",
+    "\n",
+    "# K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(512, input_dim=28*28, activation='relu'))\n",
+    "model.add(Dense(256, activation='relu'))\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dense(32, activation='relu'))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = model.fit(X_train, y_train_cat, batch_size=128, epochs=10, verbose=1, validation_split=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(h.history['accuracy'])\n",
+    "plt.plot(h.history['val_accuracy'])\n",
+    "plt.legend(['Training', 'Validation'])\n",
+    "plt.title('Accuracy')\n",
+    "plt.xlabel('Epochs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_accuracy = model.evaluate(X_test, y_test_cat)[1]\n",
+    "test_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tensor Math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.random.randint(10, size=(2, 3, 4, 5))\n",
+    "B = np.random.randint(10, size=(2, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A[0, 1, 0, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A random colored image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = np.random.randint(255, size=(4, 4, 3), dtype='uint8')\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.subplot(221)\n",
+    "plt.imshow(img)\n",
+    "plt.title(\"All Channels combined\")\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "plt.imshow(img[:, : , 0], cmap='Reds')\n",
+    "plt.title(\"Red channel\")\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.imshow(img[:, : , 1], cmap='Greens')\n",
+    "plt.title(\"Green channel\")\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "plt.imshow(img[:, : , 2], cmap='Blues')\n",
+    "plt.title(\"Blue channel\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tensor operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 * A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A + A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.tensordot(A, B, axes=([0, 1], [0, 1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.tensordot(A, B, axes=([0], [0])).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1D convolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([-1, 1], dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = np.convolve(a, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplot(211)\n",
+    "plt.plot(a, 'o-')\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.plot(c, 'o-');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Image filters with convolutions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.ndimage.filters import convolve\n",
+    "from scipy.signal import convolve2d\n",
+    "from scipy import misc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = misc.ascent()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(img, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h_kernel = np.array([[ 1,  2,  1],\n",
+    "                     [ 0,  0,  0],\n",
+    "                     [-1, -2, -1]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(h_kernel, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = convolve2d(img, h_kernel)\n",
+    "\n",
+    "plt.imshow(res, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convolutional neural networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Conv2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.imshow(img, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_tensor = img.reshape((1, 512, 512, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(1, (3, 3), strides=(2,1), input_shape=(512, 512, 1)))\n",
+    "model.compile('adam', 'mse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred_tensor = model.predict(img_tensor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred_tensor.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred = img_pred_tensor[0, :, :, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(img_pred, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights = model.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(weights[0][:, :, 0, 0], cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights[0] = np.ones(weights[0].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.set_weights(weights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred_tensor = model.predict(img_tensor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred = img_pred_tensor[0, :, :, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(img_pred, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(1, (3, 3), input_shape=(512, 512, 1), padding='same'))\n",
+    "model.compile('adam', 'mse')\n",
+    "\n",
+    "img_pred_tensor = model.predict(img_tensor)\n",
+    "\n",
+    "\n",
+    "img_pred_tensor.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pooling layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import MaxPool2D, AvgPool2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(MaxPool2D((5, 5), input_shape=(512, 512, 1)))\n",
+    "model.compile('adam', 'mse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred = model.predict(img_tensor)[0, :, :, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(img_pred, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(AvgPool2D((5, 5), input_shape=(512, 512, 1)))\n",
+    "model.compile('adam', 'mse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_pred = model.predict(img_tensor)[0, :, :, 0]\n",
+    "plt.imshow(img_pred, cmap='gray');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final architecture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.reshape(-1, 28, 28, 1)\n",
+    "X_test = X_test.reshape(-1, 28, 28, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Flatten, Activation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))\n",
+    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+    "model.add(Activation('relu'))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train_cat, batch_size=128,\n",
+    "          epochs=2, verbose=1, validation_split=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.evaluate(X_test, y_test_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Exercise 1\n",
+    "You've been hired by a shipping company to overhaul the way they route mail, parcels and packages. They want to build an image recognition system  capable of recognizing the digits in the zipcode on a package, so that it can be automatically routed to the correct location.\n",
+    "You are tasked to build the digit recognition system. Luckily, you can rely on the MNIST dataset for the intial training of your model!\n",
+    "\n",
+    "Build a deep convolutional neural network with at least two convolutional and two pooling layers before the fully connected layer.\n",
+    "\n",
+    "- Start from the network we have just built\n",
+    "- Insert a `Conv2D` layer after the first `MaxPool2D`, give it 64 filters.\n",
+    "- Insert a `MaxPool2D` after that one\n",
+    "- Insert an `Activation` layer\n",
+    "- retrain the model\n",
+    "- does performance improve?\n",
+    "- how many parameters does this new model have? More or less than the previous model? Why?\n",
+    "- how long did this second model take to train? Longer or shorter than the previous model? Why?\n",
+    "- did it perform better or worse than the previous model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 2\n",
+    "\n",
+    "Pleased with your performance with the digits recognition task, your boss decides to challenge you with a harder task. Their online branch allows people to upload images to a website that generates and prints a postcard that is shipped to destination. Your boss would like to know what images people are loading on the site in order to provide targeted advertising on the same page, so he asks you to build an image recognition system capable of recognizing a few objects. Luckily for you, there's a dataset ready made with a collection of labeled images. This is the [Cifar 10 Dataset](http://www.cs.toronto.edu/~kriz/cifar.html), a very famous dataset that contains images for 10 different categories:\n",
+    "\n",
+    "- airplane \t\t\t\t\t\t\t\t\t\t\n",
+    "- automobile \t\t\t\t\t\t\t\t\t\t\n",
+    "- bird \t\t\t\t\t\t\t\t\t\t\n",
+    "- cat \t\t\t\t\t\t\t\t\t\t\n",
+    "- deer \t\t\t\t\t\t\t\t\t\t\n",
+    "- dog \t\t\t\t\t\t\t\t\t\t\n",
+    "- frog \t\t\t\t\t\t\t\t\t\t\n",
+    "- horse \t\t\t\t\t\t\t\t\t\t\n",
+    "- ship \t\t\t\t\t\t\t\t\t\t\n",
+    "- truck\n",
+    "\n",
+    "In this exercise we will reach the limit of what you can achieve on your laptop and get ready for the next session on cloud GPUs.\n",
+    "\n",
+    "Here's what you have to do:\n",
+    "- load the cifar10 dataset using `keras.datasets.cifar10.load_data()`\n",
+    "- display a few images, see how hard/easy it is for you to recognize an object with such low resolution\n",
+    "- check the shape of X_train, does it need reshape?\n",
+    "- check the scale of X_train, does it need rescaling?\n",
+    "- check the shape of y_train, does it need reshape?\n",
+    "- build a model with the following architecture, and choose the parameters and activation functions for each of the layers:\n",
+    "    - conv2d\n",
+    "    - conv2d\n",
+    "    - maxpool\n",
+    "    - conv2d\n",
+    "    - conv2d\n",
+    "    - maxpool\n",
+    "    - flatten\n",
+    "    - dense\n",
+    "    - output\n",
+    "- compile the model and check the number of parameters\n",
+    "- attempt to train the model with the optimizer of your choice. How fast does training proceed?\n",
+    "- If training is too slow (as expected) stop the execution and move to the next session!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import cifar10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/8 Recurrent Neural Networks.ipynb b/course/8 Recurrent Neural Networks.ipynb
new file mode 100644
index 0000000..71232f9
--- /dev/null
+++ b/course/8 Recurrent Neural Networks.ipynb	
@@ -0,0 +1,565 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recurrent Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time series forecasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',\n",
+    "                 skiprows=6, skipfooter=9,\n",
+    "                 engine='python')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.offsets import MonthEnd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Adjustments'] = pd.to_datetime(df['Adjustments']) + MonthEnd(1)\n",
+    "df = df.set_index('Adjustments')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_date = pd.Timestamp('01-01-2011')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = df.loc[:split_date, ['Unadjusted']]\n",
+    "test = df.loc[split_date:, ['Unadjusted']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = train.plot()\n",
+    "test.plot(ax=ax)\n",
+    "plt.legend(['train', 'test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "sc = MinMaxScaler()\n",
+    "\n",
+    "train_sc = sc.fit_transform(train)\n",
+    "test_sc = sc.transform(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sc[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = train_sc[:-1]\n",
+    "y_train = train_sc[1:]\n",
+    "\n",
+    "X_test = test_sc[:-1]\n",
+    "y_test = test_sc[1:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fully connected predictor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "import tensorflow.keras.backend as K\n",
+    "from tensorflow.keras.callbacks import EarlyStopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(12, input_dim=1, activation='relu'))\n",
+    "model.add(Dense(1))\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=200,\n",
+    "          batch_size=2, verbose=1,\n",
+    "          callbacks=[early_stop])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recurrent predictor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#3D tensor with shape (batch_size, timesteps, input_dim)\n",
+    "X_train[:, None].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_t = X_train[:, None]\n",
+    "X_test_t = X_test[:, None]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(LSTM(6, input_shape=(1, 1)))\n",
+    "\n",
+    "model.add(Dense(1))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train_t, y_train,\n",
+    "          epochs=100, batch_size=1, verbose=1,\n",
+    "          callbacks=[early_stop])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test_t)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Windows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sc.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sc_df = pd.DataFrame(train_sc, columns=['Scaled'], index=train.index)\n",
+    "test_sc_df = pd.DataFrame(test_sc, columns=['Scaled'], index=test.index)\n",
+    "train_sc_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for s in range(1, 13):\n",
+    "    train_sc_df['shift_{}'.format(s)] = train_sc_df['Scaled'].shift(s)\n",
+    "    test_sc_df['shift_{}'.format(s)] = test_sc_df['Scaled'].shift(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sc_df.head(13)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = train_sc_df.dropna().drop('Scaled', axis=1)\n",
+    "y_train = train_sc_df.dropna()[['Scaled']]\n",
+    "\n",
+    "X_test = test_sc_df.dropna().drop('Scaled', axis=1)\n",
+    "y_test = test_sc_df.dropna()[['Scaled']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.values\n",
+    "X_test= X_test.values\n",
+    "\n",
+    "y_train = y_train.values\n",
+    "y_test = y_test.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fully Connected on Windows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(12, input_dim=12, activation='relu'))\n",
+    "model.add(Dense(1))\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=200,\n",
+    "          batch_size=1, verbose=1, callbacks=[early_stop])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LSTM on Windows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_t = X_train.reshape(X_train.shape[0], 1, 12)\n",
+    "X_test_t = X_test.reshape(X_test.shape[0], 1, 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(LSTM(6, input_shape=(1, 12)))\n",
+    "\n",
+    "model.add(Dense(1))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train_t, y_train, epochs=100,\n",
+    "          batch_size=1, verbose=1, callbacks=[early_stop])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test_t)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "In the model above we reshaped the input shape to: `(num_samples, 1, 12)`, i.e. we treated a window of 12 months as a vector of 12 coordinates that we simultaneously passed to all the LSTM nodes. An alternative way to look at the problem is to reshape the input to `(num_samples, 12, 1)`. This means we consider each input window as a sequence of 12 values that we will pass in sequence to the LSTM. In principle this looks like a more accurate description of our situation. But does it yield better predictions? Let's check it.\n",
+    "\n",
+    "- Reshape `X_train` and `X_test` so that they represent a set of univariate sequences\n",
+    "- retrain the same LSTM(6) model, you'll have to adapt the `input_shape`\n",
+    "- check the performance of this new model, is it better at predicting the test data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "RNN models can be applied to images too. In general we can apply them to any data where there's a connnection between nearby units. Let's see how we can easily build a model that works with images.\n",
+    "\n",
+    "- Load the MNIST data, by now you should be able to do it blindfolded :)\n",
+    "- reshape it so that an image looks like a long sequence of pixels\n",
+    "- create a recurrent model and train it on the training data\n",
+    "- how does it perform compared to a fully connected? How does it compare to Convolutional Neural Networks?\n",
+    "\n",
+    "(feel free to run this exercise on a cloud GPU if it's too slow on your laptop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/9 Improving performance.ipynb b/course/9 Improving performance.ipynb
new file mode 100644
index 0000000..9a7559f
--- /dev/null
+++ b/course/9 Improving performance.ipynb	
@@ -0,0 +1,872 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 9 Improving performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = load_digits()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = digits.data, digits.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(8):\n",
+    "    plt.subplot(1,8,i+1)\n",
+    "    plt.imshow(X.reshape(-1, 8, 8)[i], cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "import tensorflow.keras.backend as K\n",
+    "from tensorflow.keras.callbacks import EarlyStopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(16, input_shape=(64,), activation='relu'))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# store the initial random weights\n",
+    "initial_weights = model.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat = to_categorical(y, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_cat,\n",
+    "                                                    test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sizes = (len(X_train) * np.linspace(0.1, 0.999, 4)).astype(int)\n",
+    "train_sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_scores = []\n",
+    "test_scores = []\n",
+    "\n",
+    "for train_size in train_sizes:\n",
+    "    X_train_frac, _, y_train_frac, _ = \\\n",
+    "    train_test_split(X_train, y_train, train_size=train_size)\n",
+    "    \n",
+    "    # at each iteration reset the weights of the model\n",
+    "    # to the initial random weights\n",
+    "    model.set_weights(initial_weights)\n",
+    "    \n",
+    "    h = model.fit(X_train_frac, y_train_frac,\n",
+    "                  verbose=0,\n",
+    "                  epochs=300,\n",
+    "                  callbacks=[EarlyStopping(monitor='loss', patience=1)])\n",
+    "\n",
+    "    r = model.evaluate(X_train_frac, y_train_frac, verbose=0)\n",
+    "    train_scores.append(r[-1])\n",
+    "    \n",
+    "    e = model.evaluate(X_test, y_test, verbose=0)\n",
+    "    test_scores.append(e[-1])\n",
+    "    \n",
+    "    print(\"Done size: \", train_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(train_sizes, train_scores, 'o-', label=\"Training score\")\n",
+    "plt.plot(train_sizes, test_scores, 'o-', label=\"Test score\")\n",
+    "plt.legend(loc=\"best\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Batch Normalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import BatchNormalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def repeated_training(X_train,\n",
+    "                      y_train,\n",
+    "                      X_test,\n",
+    "                      y_test,\n",
+    "                      units=512,\n",
+    "                      activation='sigmoid',\n",
+    "                      optimizer='sgd',\n",
+    "                      do_bn=False,\n",
+    "                      epochs=10,\n",
+    "                      repeats=3):\n",
+    "    histories = []\n",
+    "    \n",
+    "    for repeat in range(repeats):\n",
+    "        K.clear_session()\n",
+    "\n",
+    "        model = Sequential()\n",
+    "        \n",
+    "        # first fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        input_shape=X_train.shape[1:],\n",
+    "                        kernel_initializer='normal',\n",
+    "                        activation=activation))\n",
+    "        if do_bn:\n",
+    "            model.add(BatchNormalization())\n",
+    "\n",
+    "        # second fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        kernel_initializer='normal',\n",
+    "                        activation=activation))\n",
+    "        if do_bn:\n",
+    "            model.add(BatchNormalization())\n",
+    "\n",
+    "        # third fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        kernel_initializer='normal',\n",
+    "                        activation=activation))\n",
+    "        if do_bn:\n",
+    "            model.add(BatchNormalization())\n",
+    "\n",
+    "        # output layer\n",
+    "        model.add(Dense(10, activation='softmax'))\n",
+    "        \n",
+    "        model.compile(optimizer,\n",
+    "                      'categorical_crossentropy',\n",
+    "                      metrics=['accuracy'])\n",
+    "\n",
+    "        h = model.fit(X_train, y_train,\n",
+    "                      validation_data=(X_test, y_test),\n",
+    "                      epochs=epochs,\n",
+    "                      verbose=0)\n",
+    "        histories.append([h.history['accuracy'], h.history['val_accuracy']])\n",
+    "        print(repeat, end=' ')\n",
+    "\n",
+    "    histories = np.array(histories)\n",
+    "    \n",
+    "    # calculate mean and standard deviation across repeats:\n",
+    "    mean_acc = histories.mean(axis=0)\n",
+    "    std_acc = histories.std(axis=0)\n",
+    "    print()\n",
+    "    \n",
+    "    return mean_acc[0], std_acc[0], mean_acc[1], std_acc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_acc, std_acc, mean_acc_val, std_acc_val = \\\n",
+    "    repeated_training(X_train, y_train, X_test, y_test, do_bn=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_acc_bn, std_acc_bn, mean_acc_val_bn, std_acc_val_bn = \\\n",
+    "    repeated_training(X_train, y_train, X_test, y_test, do_bn=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_mean_std(m, s):\n",
+    "    plt.plot(m)\n",
+    "    plt.fill_between(range(len(m)), m-s, m+s, alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_mean_std(mean_acc, std_acc)\n",
+    "plot_mean_std(mean_acc_val, std_acc_val)\n",
+    "plot_mean_std(mean_acc_bn, std_acc_bn)\n",
+    "plot_mean_std(mean_acc_val_bn, std_acc_val_bn)\n",
+    "plt.ylim(0, 1.01)\n",
+    "plt.title(\"Batch Normalization Accuracy\")\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend(['Train', 'Test', 'Train with Batch Normalization', 'Test with Batch Normalization'], loc='best');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weight Regularization & Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dropout(0.2, input_shape=X_train.shape[1:]))\n",
+    "# first fully connected layer\n",
+    "model.add(Dense(512, kernel_initializer='normal',\n",
+    "                kernel_regularizer='l2', activation='sigmoid'))\n",
+    "model.add(Dropout(0.4))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile('sgd',\n",
+    "              'categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data augmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "generator = ImageDataGenerator(rescale = 1./255,\n",
+    "                               width_shift_range=0.1,\n",
+    "                               height_shift_range=0.1,\n",
+    "                               rotation_range = 20,\n",
+    "                               shear_range = 0.3,\n",
+    "                               zoom_range = 0.3,\n",
+    "                               horizontal_flip = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = generator.flow_from_directory('../data/generator',\n",
+    "                                      target_size = (128, 128),\n",
+    "                                      batch_size = 32,\n",
+    "                                      class_mode = 'binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 12))\n",
+    "for i in range(16):\n",
+    "    img, label = train.next()\n",
+    "    plt.subplot(4, 4, i+1)\n",
+    "    plt.imshow(img[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Embedding(input_dim=100, output_dim=2))\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer='adam',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb = model.predict(np.array([[81,  1, 96, 79],\n",
+    "                              [17, 47, 69, 50],\n",
+    "                              [49,  3, 12, 88]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sentiment prediction on movie Reviews"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import imdb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = imdb.load_data('/tmp/imdb.npz',\n",
+    "                                                      num_words=None,\n",
+    "                                                      skip_top=0,\n",
+    "                                                      maxlen=None,\n",
+    "                                                      start_char=1,\n",
+    "                                                      oov_char=2,\n",
+    "                                                      index_from=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = imdb.get_word_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max(idx.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_idx = {v+3:k for k,v in idx.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_idx[0] = 'padding_char'\n",
+    "rev_idx[1] = 'start_char'\n",
+    "rev_idx[2] = 'oov_char'\n",
+    "rev_idx[3] = 'unk_char'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_idx[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_review = ' '.join([rev_idx[word] for word in X_train[0]])\n",
+    "example_review"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+    "from tensorflow.keras.layers import LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxlen = 100\n",
+    "\n",
+    "X_train_pad = pad_sequences(X_train, maxlen=maxlen)\n",
+    "X_test_pad = pad_sequences(X_test, maxlen=maxlen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_pad.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_pad[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_features = max([max(x) for x in X_train_pad] + \n",
+    "                   [max(x) for x in X_test_pad]) + 1\n",
+    "max_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Embedding(max_features, 128))\n",
+    "model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer='adam',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train_pad, y_train,\n",
+    "          batch_size=32,\n",
+    "          epochs=2,\n",
+    "          validation_split=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score, acc = model.evaluate(X_test_pad, y_test)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "- Reload the IMDB data keeping only the first 20000 most common words\n",
+    "- pad the reviews to a shorter length (eg. 70 or 80), this time make sure you keep the first part of the review if it's longer than the maximum length\n",
+    "- re run the model (remember to set max_features correctly)\n",
+    "- does it train faster this time?\n",
+    "- do you get a better performance?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "- Reload the digits data as above\n",
+    "- define a function repeated_training_reg_dropout that adds regularization and dropout to a fully connected network\n",
+    "- compare the performance with/witouth dropout and regularization like we did for batch normalization\n",
+    "- do you get a better performance?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "\n",
+    "This is a very long and complex exercise, that should give you an idea of a real world scenario. Feel free to look at the solution if you feel lost. Also, feel free to run this on Floyd with a GPU, in which case you don't need to download the data.\n",
+    "\n",
+    "If you are running this locally, download and unpack the male/female pictures from [here](https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=0). These images and labels were obtained from [Crowdflower](https://www.crowdflower.com/data-for-everyone/).\n",
+    "\n",
+    "Your goal is to build an image classifier that will recognize the gender of a person from pictures.\n",
+    "\n",
+    "- Have a look at the directory structure and inspect a couple of pictures\n",
+    "- Design a model that will take a color image of size 64x64 as input and return a binary output (female=0/male=1)\n",
+    "- Feel free to introduce any regularization technique in your model (Dropout, Batch Normalization, Weight Regularization)\n",
+    "- Compile your model with an optimizer of your choice\n",
+    "- Using `ImageDataGenerator`, define a train generator that will augment your images with some geometric transformations. Feel free to choose the parameters that make sense to you.\n",
+    "- Define also a test generator, whose only purpose is to rescale the pixels by 1./255\n",
+    "- use the function `flow_from_directory` to generate batches from the train and test folders. Make sure you set the `target_size` to 64x64.\n",
+    "- Use the `model.fit_generator` function to fit the model on the batches generated from the ImageDataGenerator. Since you are streaming and augmenting the data in real time you will have to decide how many batches make an epoch and how many epochs you want to run\n",
+    "- Train your model (you should get to at least 85% accuracy)\n",
+    "- Once you are satisfied with your training, check a few of the misclassified pictures. Are those sensible errors?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/HR_comma_sep.csv b/data/HR_comma_sep.csv
new file mode 100644
index 0000000..be6211c
--- /dev/null
+++ b/data/HR_comma_sep.csv
@@ -0,0 +1,15000 @@
+satisfaction_level,last_evaluation,number_project,average_montly_hours,time_spend_company,Work_accident,left,promotion_last_5years,sales,salary
+0.38,0.53,2,157,3,0,1,0,sales,low
+0.8,0.86,5,262,6,0,1,0,sales,medium
+0.11,0.88,7,272,4,0,1,0,sales,medium
+0.72,0.87,5,223,5,0,1,0,sales,low
+0.37,0.52,2,159,3,0,1,0,sales,low
+0.41,0.5,2,153,3,0,1,0,sales,low
+0.1,0.77,6,247,4,0,1,0,sales,low
+0.92,0.85,5,259,5,0,1,0,sales,low
+0.89,1,5,224,5,0,1,0,sales,low
+0.42,0.53,2,142,3,0,1,0,sales,low
+0.45,0.54,2,135,3,0,1,0,sales,low
+0.11,0.81,6,305,4,0,1,0,sales,low
+0.84,0.92,4,234,5,0,1,0,sales,low
+0.41,0.55,2,148,3,0,1,0,sales,low
+0.36,0.56,2,137,3,0,1,0,sales,low
+0.38,0.54,2,143,3,0,1,0,sales,low
+0.45,0.47,2,160,3,0,1,0,sales,low
+0.78,0.99,4,255,6,0,1,0,sales,low
+0.45,0.51,2,160,3,1,1,1,sales,low
+0.76,0.89,5,262,5,0,1,0,sales,low
+0.11,0.83,6,282,4,0,1,0,sales,low
+0.38,0.55,2,147,3,0,1,0,sales,low
+0.09,0.95,6,304,4,0,1,0,sales,low
+0.46,0.57,2,139,3,0,1,0,sales,low
+0.4,0.53,2,158,3,0,1,0,sales,low
+0.89,0.92,5,242,5,0,1,0,sales,low
+0.82,0.87,4,239,5,0,1,0,sales,low
+0.4,0.49,2,135,3,0,1,0,sales,low
+0.41,0.46,2,128,3,0,1,0,accounting,low
+0.38,0.5,2,132,3,0,1,0,accounting,low
+0.09,0.62,6,294,4,0,1,0,accounting,low
+0.45,0.57,2,134,3,0,1,0,hr,low
+0.4,0.51,2,145,3,0,1,0,hr,low
+0.45,0.55,2,140,3,0,1,0,hr,low
+0.84,0.87,4,246,6,0,1,0,hr,low
+0.1,0.94,6,255,4,0,1,0,technical,low
+0.38,0.46,2,137,3,0,1,0,technical,low
+0.45,0.5,2,126,3,0,1,0,technical,low
+0.11,0.89,6,306,4,0,1,0,technical,low
+0.41,0.54,2,152,3,0,1,0,technical,low
+0.87,0.88,5,269,5,0,1,0,technical,low
+0.45,0.48,2,158,3,0,1,0,technical,low
+0.4,0.46,2,127,3,0,1,0,technical,low
+0.1,0.8,7,281,4,0,1,0,technical,low
+0.09,0.89,6,276,4,0,1,0,technical,low
+0.84,0.74,3,182,4,0,1,0,technical,low
+0.4,0.55,2,147,3,0,1,0,support,low
+0.57,0.7,3,273,6,0,1,0,support,low
+0.4,0.54,2,148,3,0,1,0,support,low
+0.43,0.47,2,147,3,0,1,0,support,low
+0.13,0.78,6,152,2,0,1,0,support,low
+0.44,0.55,2,135,3,0,1,0,support,low
+0.38,0.55,2,134,3,0,1,0,support,low
+0.39,0.54,2,132,3,0,1,0,support,low
+0.1,0.92,7,307,4,0,1,0,support,low
+0.37,0.46,2,140,3,0,1,0,support,low
+0.11,0.94,7,255,4,0,1,0,support,low
+0.1,0.81,6,309,4,0,1,0,technical,low
+0.38,0.54,2,128,3,0,1,0,technical,low
+0.85,1,4,225,5,0,1,0,technical,low
+0.85,0.91,5,226,5,0,1,0,management,medium
+0.11,0.93,7,308,4,0,1,0,IT,medium
+0.1,0.95,6,244,5,0,1,0,IT,medium
+0.36,0.56,2,132,3,0,1,0,IT,medium
+0.11,0.94,6,286,4,0,1,0,IT,medium
+0.81,0.7,6,161,4,0,1,0,IT,medium
+0.43,0.54,2,153,3,0,1,0,product_mng,medium
+0.9,0.98,4,264,6,0,1,0,product_mng,medium
+0.76,0.86,5,223,5,1,1,0,product_mng,medium
+0.43,0.5,2,135,3,0,1,0,product_mng,medium
+0.74,0.99,2,277,3,0,1,0,IT,medium
+0.09,0.77,5,275,4,0,1,0,product_mng,medium
+0.45,0.49,2,149,3,0,1,0,product_mng,high
+0.09,0.87,7,295,4,0,1,0,product_mng,low
+0.11,0.97,6,277,4,0,1,0,product_mng,medium
+0.11,0.79,7,306,4,0,1,0,product_mng,medium
+0.1,0.83,6,295,4,0,1,0,product_mng,medium
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+0.43,0.56,2,157,3,0,1,0,sales,low
+0.39,0.56,2,142,3,0,1,0,accounting,low
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+0.84,0.85,4,249,6,0,1,0,marketing,low
+0.11,0.77,6,291,4,0,1,0,marketing,low
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+0.11,0.97,6,284,4,0,1,0,sales,low
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+0.09,0.94,7,267,4,0,1,0,sales,medium
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+0.24,0.46,7,224,5,0,1,0,accounting,medium
+0.91,1,4,257,5,0,1,0,accounting,medium
+0.44,0.5,2,148,3,0,1,0,accounting,medium
+0.71,0.87,3,177,4,0,1,0,hr,medium
+0.4,0.49,2,155,3,0,1,0,hr,medium
+0.43,0.47,2,144,3,0,1,0,hr,medium
+0.09,0.85,6,289,4,0,1,0,hr,high
+0.43,0.52,2,160,3,0,1,0,technical,low
+0.9,0.96,4,258,5,0,1,0,technical,medium
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\ No newline at end of file
diff --git a/data/banknotes.csv b/data/banknotes.csv
new file mode 100644
index 0000000..c532ca7
--- /dev/null
+++ b/data/banknotes.csv
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+-2.6406,-4.4159,5.983,-0.13924,1
+-4.6338,-12.7509,16.7166,-3.2168,1
+-4.2887,-7.8633,11.8387,-1.8978,1
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+-1.6029,-0.38903,1.62,1.9103,1
+-1.2667,2.8183,-2.426,-1.8862,1
+-0.49281,3.0605,-1.8356,-2.834,1
+0.66365,-0.045533,-0.18794,0.23447,1
+-0.72068,-6.7583,5.8408,0.62369,1
+-1.9966,-9.5001,9.682,-0.12889,1
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+-0.025314,-0.17383,-0.11339,1.2198,1
+0.062525,2.9301,-3.5467,-2.6737,1
+-5.525,6.3258,0.89768,-6.6241,1
+-1.2943,2.6735,-0.84085,-2.0323,1
+-0.24037,-1.7837,2.135,1.2418,1
+-1.3968,-9.6698,9.4652,-0.34872,1
+-2.9672,-13.2869,13.4727,-2.6271,1
+-1.1005,-7.2508,6.0139,0.36895,1
+0.22432,-0.52147,-0.40386,1.2017,1
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+1.581,0.86909,-2.3138,0.82412,1
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+-4.1479,7.1225,-0.083404,-6.4172,1
+-2.2625,-0.099335,2.8127,0.48662,1
+-1.7479,-5.823,5.8699,1.212,1
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+1.3451,0.23589,-1.8785,1.3258,1
+2.2279,4.0951,-4.8037,-2.1112,1
+1.2572,4.8731,-5.2861,-5.8741,1
+-5.3857,9.1214,-0.41929,-5.9181,1
+-2.9786,2.3445,0.52667,-0.40173,1
+-1.5851,-2.1562,1.7082,0.9017,1
+-0.21888,-2.2038,-0.0954,0.56421,1
+1.3183,1.9017,-3.3111,0.065071,1
+1.4896,3.4288,-4.0309,-1.4259,1
+0.11592,3.2219,-3.4302,-2.8457,1
+-3.3924,3.3564,-0.72004,-3.5233,1
+-6.1632,8.7096,-0.21621,-3.6345,1
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\ No newline at end of file
diff --git a/data/banknotes.png b/data/banknotes.png
new file mode 100644
index 0000000..ca7833b
Binary files /dev/null and b/data/banknotes.png differ
diff --git a/data/cansim-0800020-eng-6674700030567901031.csv b/data/cansim-0800020-eng-6674700030567901031.csv
new file mode 100644
index 0000000..d821155
--- /dev/null
+++ b/data/cansim-0800020-eng-6674700030567901031.csv
@@ -0,0 +1,329 @@
+"Table 080-0020 Retail trade, sales by the North American Industry Classification System (NAICS), monthly (dollars x 1,000)(2,3,4,5,6)"
+Survey or program details:
+Retail Trade Survey (Monthly) - 2406
+Monthly Retail Trade Survey (Department Store Organizations) - 2408
+Geography,Canada,Canada
+North American Industry Classification System (NAICS),Retail trade [44-45] ,Retail trade [44-45] 
+Adjustments,Unadjusted,Seasonally adjusted
+Jan-1991,12588862,15026890
+Feb-1991,12154321,15304585
+Mar-1991,14337072,15413591
+Apr-1991,15108570,15293409
+May-1991,17225734,15676083
+Jun-1991,16342833,15507931
+Jul-1991,15996243,15556313
+Aug-1991,16064910,15430645
+Sep-1991,15015317,15427313
+Oct-1991,15606864,15410250
+Nov-1991,16237366,15662790
+Dec-1991,18381340,15349625
+Jan-1992,13084963,15477875
+Feb-1992,12773972,15513022
+Mar-1992,14198775,15527933
+Apr-1992,15558390,15708556
+May-1992,16776396,15642000
+Jun-1992,16716231,15823989
+Jul-1992,16637483,15869453
+Aug-1992,15842075,15844631
+Sep-1992,15812400,15983239
+Oct-1992,16562268,16125835
+Nov-1992,16015869,16049478
+Dec-1992,19682921,16095727
+Jan-1993,13672727,16408864
+Feb-1993,12900733,16239039
+Mar-1993,15211859,16314960
+Apr-1993,16642246,16577426
+May-1993,17442405,16472045
+Jun-1993,17444074,16351907
+Jul-1993,17610326,16712914
+Aug-1993,16645660,16703413
+Sep-1993,16790330,16755338
+Oct-1993,16921755,16819382
+Nov-1993,17124609,16958202
+Dec-1993,20928208,17021436
+Jan-1994,14005058,17076164
+Feb-1994,13799079,17393150
+Mar-1994,16865149,17890903
+Apr-1994,17494589,17507688
+May-1994,18739509,17775079
+Jun-1994,19323481,17882069
+Jul-1994,18297834,17785800
+Aug-1994,18101290,17881976
+Sep-1994,18161417,17952647
+Oct-1994,17998875,18193703
+Nov-1994,18516766,18264676
+Dec-1994,22688647,18387840
+Jan-1995,14927996,18337565
+Feb-1995,14520623,18259470
+Mar-1995,17457477,18225708
+Apr-1995,17774107,18217661
+May-1995,19740889,18333051
+Jun-1995,20319460,18503481
+Jul-1995,18747299,18407254
+Aug-1995,19280525,18720783
+Sep-1995,18860566,18628735
+Oct-1995,18177152,18412692
+Nov-1995,18962903,18506305
+Dec-1995,22308880,18525162
+Jan-1996,15379086,18531426
+Feb-1996,15521981,18657652
+Mar-1996,17613469,18774049
+Apr-1996,18421405,18739023
+May-1996,20624568,18758009
+Jun-1996,20099348,18977805
+Jul-1996,19423284,18914063
+Aug-1996,19889359,19071178
+Sep-1996,18589571,19019991
+Oct-1996,19686383,19488074
+Nov-1996,20293165,19820074
+Dec-1996,22897980,19688254
+Jan-1997,16882321,19857365
+Feb-1997,16033605,20141489
+Mar-1997,18225453,20056949
+Apr-1997,20432272,20215340
+May-1997,22594727,20386953
+Jun-1997,21577744,20466883
+Jul-1997,21570145,20681230
+Aug-1997,21065784,20605349
+Sep-1997,20532806,20646564
+Oct-1997,21491163,21037021
+Nov-1997,20904746,20973561
+Dec-1997,25507180,21749250
+Jan-1998,17736224,20776214
+Feb-1998,16797018,21153779
+Mar-1998,19408883,21041225
+Apr-1998,21501677,21504619
+May-1998,23312947,21504262
+Jun-1998,22654803,21247311
+Jul-1998,22594775,21385620
+Aug-1998,21512734,21335980
+Sep-1998,21645562,21660645
+Oct-1998,21994089,21565457
+Nov-1998,21461344,21714061
+Dec-1998,25874332,21605209
+Jan-1999,18438151,22074043
+Feb-1999,17658952,22286260
+Mar-1999,21082603,22402680
+Apr-1999,22587382,22389229
+May-1999,23892100,22300484
+Jun-1999,24036828,22450487
+Jul-1999,23994614,22614164
+Aug-1999,22926469,22806183
+Sep-1999,22984278,22817165
+Oct-1999,22813633,22967565
+Nov-1999,22972959,23036527
+Dec-1999,28143999,23387176
+Jan-2000,19324692,23434451
+Feb-2000,19140440,23378327
+Mar-2000,22918829,23813646
+Apr-2000,22914155,23537859
+May-2000,25659687,23644020
+Jun-2000,25945400,23841011
+Jul-2000,24821347,24204193
+Aug-2000,25102965,24266358
+Sep-2000,24710257,24495699
+Oct-2000,23687124,24330740
+Nov-2000,24556357,24373656
+Dec-2000,29057176,24518477
+Jan-2001,20607642,24640517
+Feb-2001,19444855,24477976
+Mar-2001,23652255,24583988
+Apr-2001,24370700,24944482
+May-2001,27585889,25143416
+Jun-2001,27243919,25190078
+Jul-2001,25507932,24813831
+Aug-2001,26322941,25017925
+Sep-2001,24263969,24703734
+Oct-2001,24917747,25149772
+Nov-2001,26048646,25657476
+Dec-2001,30481412,26124705
+Jan-2002,22361219,26496729
+Feb-2002,20787209,26105990
+Mar-2002,24642692,26072633
+Apr-2002,26405170,26657565
+May-2002,29087583,26165998
+Jun-2002,28363263,26768924
+Jul-2002,27912328,26620588
+Aug-2002,28202300,26767279
+Sep-2002,26054411,26620259
+Oct-2002,27131743,27061844
+Nov-2002,27276942,27015215
+Dec-2002,31300554,27172388
+Jan-2003,23301871,27137545
+Feb-2003,21980804,27622538
+Mar-2003,25468203,27275170
+Apr-2003,27059495,27176306
+May-2003,30417563,27484615
+Jun-2003,28912102,27569750
+Jul-2003,29492832,27707861
+Aug-2003,29102135,28020755
+Sep-2003,27467571,27841344
+Oct-2003,28223631,27825024
+Nov-2003,27391422,27777531
+Dec-2003,32325789,27704978
+Jan-2004,23778728,27935993
+Feb-2004,23008594,28719948
+Mar-2004,26967793,28689514
+Apr-2004,28592026,28254086
+May-2004,30479247,28554094
+Jun-2004,30711705,28550528
+Jul-2004,30898334,28616168
+Aug-2004,29535183,28836665
+Sep-2004,29245397,29243662
+Oct-2004,29445711,29561177
+Nov-2004,29232659,29901036
+Dec-2004,34561103,29593609
+Jan-2005,24498615,29888781
+Feb-2005,24028226,30460620
+Mar-2005,28600602,30006264
+Apr-2005,30600811,29940271
+May-2005,31948565,29935878
+Jun-2005,32967426,30590992
+Jul-2005,32620077,30800241
+Aug-2005,32025283,30647393
+Sep-2005,30914826,30600008
+Oct-2005,30241532,30887481
+Nov-2005,30828069,31012789
+Dec-2005,36726743,31230056
+Jan-2006,25993203,31747679
+Feb-2006,25128165,31744450
+Mar-2006,30760061,31871717
+Apr-2006,32106585,32354405
+May-2006,34894460,32048264
+Jun-2006,35049112,32242363
+Jul-2006,34341547,33040218
+Aug-2006,35045180,33007575
+Sep-2006,33056559,32492838
+Oct-2006,31830349,32595465
+Nov-2006,32663281,32814138
+Dec-2006,38605976,33515367
+Jan-2007,27777968,33221023
+Feb-2007,26548520,33466188
+Mar-2007,32818504,33910296
+Apr-2007,33621240,34385868
+May-2007,38434319,34789277
+Jun-2007,37555708,34436278
+Jul-2007,35635889,34430726
+Aug-2007,36978090,34725483
+Sep-2007,34057842,34341226
+Oct-2007,34070363,34305870
+Nov-2007,35091406,34958995
+Dec-2007,40006665,35625285
+Jan-2008,30525699,35933156
+Feb-2008,29418898,35526909
+Mar-2008,32925876,35530142
+Apr-2008,36272111,35914167
+May-2008,39778972,36006888
+Jun-2008,37842321,36400129
+Jul-2008,38632038,36403867
+Aug-2008,37775417,36137537
+Sep-2008,36138751,36390521
+Oct-2008,36158245,35747571
+Nov-2008,34230901,34661690
+Dec-2008,38256712,33303365
+Jan-2009,29192654,33747849
+Feb-2009,26804723,33869426
+Mar-2009,31356949,33894022
+Apr-2009,33942769,33930673
+May-2009,37316515,34345160
+Jun-2009,36865690,34735113
+Jul-2009,37191480,34755444
+Aug-2009,36049418,35039739
+Sep-2009,35537357,35239195
+Oct-2009,36133694,35330869
+Nov-2009,34354756,35250896
+Dec-2009,40969765,35577384
+Jan-2010,30668321,36080033
+Feb-2010,28632551,36051781
+Mar-2010,34967182,37001843
+Apr-2010,36469949,36148495
+May-2010,38424455,36041318
+Jun-2010,38973462,36350588
+Jul-2010,38932294,36295314
+Aug-2010,37395330,36515170
+Sep-2010,36923390,36632898
+Oct-2010,37014326,36879707
+Nov-2010,37408825,37568029
+Dec-2010,43147947,37392857
+Jan-2011,31191594,37392259
+Feb-2011,29797949,37437926
+Mar-2011,36099866,37617167
+Apr-2011,38035760,37755408
+May-2011,40046516,37723958
+Jun-2011,40839556,38228307
+Jul-2011,39832282,37925826
+Aug-2011,39541248,37976798
+Sep-2011,38877263,38181654
+Oct-2011,38203872,38623692
+Nov-2011,39174736,38779553
+Dec-2011,45089701,39087795
+Jan-2012,32361808,39102435
+Feb-2012,32087072,38968001
+Mar-2012,37933733,39201228
+Apr-2012,37775805,38920526
+May-2012,42584571,38841267
+Jun-2012,41789242,38773515
+Jul-2012,40130908,38854126
+Aug-2012,41321526,38854279
+Sep-2012,39069513,39058649
+Oct-2012,39487597,39277317
+Nov-2012,40095933,39224805
+Dec-2012,43489091,39050651
+Jan-2013,33574671,39523536
+Feb-2013,31636843,39710038
+Mar-2013,37561378,39811962
+Apr-2013,39401295,39655045
+May-2013,44577490,40295930
+Jun-2013,42169145,39992542
+Jul-2013,42417829,40388278
+Aug-2013,43237460,40660890
+Sep-2013,40170270,40631164
+Oct-2013,41560987,40813306
+Nov-2013,41893714,40798569
+Dec-2013,44796794,40716614
+Jan-2014,34980327,40976155
+Feb-2014,32905708,41256280
+Mar-2014,38460091,41242344
+Apr-2014,41809373,41852467
+May-2014,46379543,41906455
+Jun-2014,44178750,42457216
+Jul-2014,45285331,42562972
+Aug-2014,44359733,42456214
+Sep-2014,43017529,42685882
+Oct-2014,43775478,42690228
+Nov-2014,42968326,42603501
+Dec-2014,46887481,42317955
+Jan-2015,34820395,40971992
+Feb-2015,33174923,41801906
+Mar-2015,39444291,42420253
+Apr-2015,42297319,42331926
+May-2015,46670930,42721761
+Jun-2015,45584849,42989280
+Jul-2015,46295664,43154020
+Aug-2015,44793347,43309509
+Sep-2015,43999627,43303889
+Oct-2015,44507776,43378904
+Nov-2015,43696305,43921767
+Dec-2015,48097829,43078048
+Jan-2016,36415115,43977584
+Feb-2016,35649450,44205540
+Mar-2016,41403762,43839996
+Apr-2016,44881587,44181416
+May-2016,47337082,44176591
+Jun-2016,47399117,44162244
+Jul-2016,46321314,44110862
+Aug-2016,46201453,44216280
+Sep-2016,45528702,44534797
+Oct-2016,44770113,45061618
+Nov-2016,46285062,45141762
+Dec-2016,50016137,44943929
+Jan-2017,37628452,45952103
+Footnotes:
+2,The total for retail trade excludes North American Industry Classification System (NAICS) 454.
+3,"This CANSIM table replaces archived table 80-0014, 80-0015 and 80-0017."
+4,"Quality indicator: Code A=Excellent. Code B=Very good. Code C=Good. Code D=Acceptable. Code E=Poor, use with caution. Code F=Unreliable (data not published)."
+5,"Data for Northwest Territories includes Nunavut, from 1991-01 to 1998-12."
+6,"In April 2013, data from 2004 onwards will be based on the 2012 North American Industry Classification System (NAICS). Data prior to 2004 will continue to be based on the 2007 North American Industry Classification System (NAICS)."
+Source:
+"Statistics Canada. Table 080-0020 - Retail trade, sales by the North American Industry Classification System (NAICS), monthly (dollars)"
+"(accessed: April 19, 2017)"
diff --git a/data/diabetes.csv b/data/diabetes.csv
new file mode 100644
index 0000000..9e6a362
--- /dev/null
+++ b/data/diabetes.csv
@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
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diff --git a/data/generator/class 0/squirrel.jpeg b/data/generator/class 0/squirrel.jpeg
new file mode 100644
index 0000000..010db1a
Binary files /dev/null and b/data/generator/class 0/squirrel.jpeg differ
diff --git a/data/heart.csv b/data/heart.csv
new file mode 100644
index 0000000..c211c93
--- /dev/null
+++ b/data/heart.csv
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diff --git a/data/housing-data.csv b/data/housing-data.csv
new file mode 100644
index 0000000..d53b2ef
--- /dev/null
+++ b/data/housing-data.csv
@@ -0,0 +1,48 @@
+sqft,bdrms,age,price
+2104,3,70,399900
+1600,3,28,329900
+2400,3,44,369000
+1416,2,49,232000
+3000,4,75,539900
+1985,4,61,299900
+1534,3,12,314900
+1427,3,57,198999
+1380,3,14,212000
+1494,3,15,242500
+1940,4,7,239999
+2000,3,27,347000
+1890,3,45,329999
+4478,5,49,699900
+1268,3,58,259900
+2300,4,77,449900
+1320,2,62,299900
+1236,3,78,199900
+2609,4,5,499998
+3031,4,21,599000
+1767,3,44,252900
+1888,2,79,255000
+1604,3,13,242900
+1962,4,53,259900
+3890,3,36,573900
+1100,3,60,249900
+1458,3,29,464500
+2526,3,13,469000
+2200,3,28,475000
+2637,3,25,299900
+1839,2,40,349900
+1000,1,5,169900
+2040,4,75,314900
+3137,3,67,579900
+1811,4,24,285900
+1437,3,50,249900
+1239,3,22,229900
+2132,4,28,345000
+4215,4,66,549000
+2162,4,43,287000
+1664,2,40,368500
+2238,3,37,329900
+2567,4,57,314000
+1200,3,76,299000
+852,2,70,179900
+1852,4,64,299900
+1203,3,11,239500
diff --git a/data/international-airline-passengers.csv b/data/international-airline-passengers.csv
new file mode 100644
index 0000000..404288c
--- /dev/null
+++ b/data/international-airline-passengers.csv
@@ -0,0 +1,145 @@
+"Month","Thousand Passengers"
+"1949-01",112
+"1949-02",118
+"1949-03",132
+"1949-04",129
+"1949-05",121
+"1949-06",135
+"1949-07",148
+"1949-08",148
+"1949-09",136
+"1949-10",119
+"1949-11",104
+"1949-12",118
+"1950-01",115
+"1950-02",126
+"1950-03",141
+"1950-04",135
+"1950-05",125
+"1950-06",149
+"1950-07",170
+"1950-08",170
+"1950-09",158
+"1950-10",133
+"1950-11",114
+"1950-12",140
+"1951-01",145
+"1951-02",150
+"1951-03",178
+"1951-04",163
+"1951-05",172
+"1951-06",178
+"1951-07",199
+"1951-08",199
+"1951-09",184
+"1951-10",162
+"1951-11",146
+"1951-12",166
+"1952-01",171
+"1952-02",180
+"1952-03",193
+"1952-04",181
+"1952-05",183
+"1952-06",218
+"1952-07",230
+"1952-08",242
+"1952-09",209
+"1952-10",191
+"1952-11",172
+"1952-12",194
+"1953-01",196
+"1953-02",196
+"1953-03",236
+"1953-04",235
+"1953-05",229
+"1953-06",243
+"1953-07",264
+"1953-08",272
+"1953-09",237
+"1953-10",211
+"1953-11",180
+"1953-12",201
+"1954-01",204
+"1954-02",188
+"1954-03",235
+"1954-04",227
+"1954-05",234
+"1954-06",264
+"1954-07",302
+"1954-08",293
+"1954-09",259
+"1954-10",229
+"1954-11",203
+"1954-12",229
+"1955-01",242
+"1955-02",233
+"1955-03",267
+"1955-04",269
+"1955-05",270
+"1955-06",315
+"1955-07",364
+"1955-08",347
+"1955-09",312
+"1955-10",274
+"1955-11",237
+"1955-12",278
+"1956-01",284
+"1956-02",277
+"1956-03",317
+"1956-04",313
+"1956-05",318
+"1956-06",374
+"1956-07",413
+"1956-08",405
+"1956-09",355
+"1956-10",306
+"1956-11",271
+"1956-12",306
+"1957-01",315
+"1957-02",301
+"1957-03",356
+"1957-04",348
+"1957-05",355
+"1957-06",422
+"1957-07",465
+"1957-08",467
+"1957-09",404
+"1957-10",347
+"1957-11",305
+"1957-12",336
+"1958-01",340
+"1958-02",318
+"1958-03",362
+"1958-04",348
+"1958-05",363
+"1958-06",435
+"1958-07",491
+"1958-08",505
+"1958-09",404
+"1958-10",359
+"1958-11",310
+"1958-12",337
+"1959-01",360
+"1959-02",342
+"1959-03",406
+"1959-04",396
+"1959-05",420
+"1959-06",472
+"1959-07",548
+"1959-08",559
+"1959-09",463
+"1959-10",407
+"1959-11",362
+"1959-12",405
+"1960-01",417
+"1960-02",391
+"1960-03",419
+"1960-04",461
+"1960-05",472
+"1960-06",535
+"1960-07",622
+"1960-08",606
+"1960-09",508
+"1960-10",461
+"1960-11",390
+"1960-12",432
\ No newline at end of file
diff --git a/data/iris.csv b/data/iris.csv
new file mode 100644
index 0000000..20bd6ee
--- /dev/null
+++ b/data/iris.csv
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,species
+5.1,3.5,1.4,0.2,setosa
+4.9,3.0,1.4,0.2,setosa
+4.7,3.2,1.3,0.2,setosa
+4.6,3.1,1.5,0.2,setosa
+5.0,3.6,1.4,0.2,setosa
+5.4,3.9,1.7,0.4,setosa
+4.6,3.4,1.4,0.3,setosa
+5.0,3.4,1.5,0.2,setosa
+4.4,2.9,1.4,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+5.4,3.7,1.5,0.2,setosa
+4.8,3.4,1.6,0.2,setosa
+4.8,3.0,1.4,0.1,setosa
+4.3,3.0,1.1,0.1,setosa
+5.8,4.0,1.2,0.2,setosa
+5.7,4.4,1.5,0.4,setosa
+5.4,3.9,1.3,0.4,setosa
+5.1,3.5,1.4,0.3,setosa
+5.7,3.8,1.7,0.3,setosa
+5.1,3.8,1.5,0.3,setosa
+5.4,3.4,1.7,0.2,setosa
+5.1,3.7,1.5,0.4,setosa
+4.6,3.6,1.0,0.2,setosa
+5.1,3.3,1.7,0.5,setosa
+4.8,3.4,1.9,0.2,setosa
+5.0,3.0,1.6,0.2,setosa
+5.0,3.4,1.6,0.4,setosa
+5.2,3.5,1.5,0.2,setosa
+5.2,3.4,1.4,0.2,setosa
+4.7,3.2,1.6,0.2,setosa
+4.8,3.1,1.6,0.2,setosa
+5.4,3.4,1.5,0.4,setosa
+5.2,4.1,1.5,0.1,setosa
+5.5,4.2,1.4,0.2,setosa
+4.9,3.1,1.5,0.2,setosa
+5.0,3.2,1.2,0.2,setosa
+5.5,3.5,1.3,0.2,setosa
+4.9,3.6,1.4,0.1,setosa
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+5.0,3.3,1.4,0.2,setosa
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+6.9,3.1,4.9,1.5,versicolor
+5.5,2.3,4.0,1.3,versicolor
+6.5,2.8,4.6,1.5,versicolor
+5.7,2.8,4.5,1.3,versicolor
+6.3,3.3,4.7,1.6,versicolor
+4.9,2.4,3.3,1.0,versicolor
+6.6,2.9,4.6,1.3,versicolor
+5.2,2.7,3.9,1.4,versicolor
+5.0,2.0,3.5,1.0,versicolor
+5.9,3.0,4.2,1.5,versicolor
+6.0,2.2,4.0,1.0,versicolor
+6.1,2.9,4.7,1.4,versicolor
+5.6,2.9,3.6,1.3,versicolor
+6.7,3.1,4.4,1.4,versicolor
+5.6,3.0,4.5,1.5,versicolor
+5.8,2.7,4.1,1.0,versicolor
+6.2,2.2,4.5,1.5,versicolor
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+6.4,2.7,5.3,1.9,virginica
+6.8,3.0,5.5,2.1,virginica
+5.7,2.5,5.0,2.0,virginica
+5.8,2.8,5.1,2.4,virginica
+6.4,3.2,5.3,2.3,virginica
+6.5,3.0,5.5,1.8,virginica
+7.7,3.8,6.7,2.2,virginica
+7.7,2.6,6.9,2.3,virginica
+6.0,2.2,5.0,1.5,virginica
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+5.6,2.8,4.9,2.0,virginica
+7.7,2.8,6.7,2.0,virginica
+6.3,2.7,4.9,1.8,virginica
+6.7,3.3,5.7,2.1,virginica
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+7.9,3.8,6.4,2.0,virginica
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+6.1,2.6,5.6,1.4,virginica
+7.7,3.0,6.1,2.3,virginica
+6.3,3.4,5.6,2.4,virginica
+6.4,3.1,5.5,1.8,virginica
+6.0,3.0,4.8,1.8,virginica
+6.9,3.1,5.4,2.1,virginica
+6.7,3.1,5.6,2.4,virginica
+6.9,3.1,5.1,2.3,virginica
+5.8,2.7,5.1,1.9,virginica
+6.8,3.2,5.9,2.3,virginica
+6.7,3.3,5.7,2.5,virginica
+6.7,3.0,5.2,2.3,virginica
+6.3,2.5,5.0,1.9,virginica
+6.5,3.0,5.2,2.0,virginica
+6.2,3.4,5.4,2.3,virginica
+5.9,3.0,5.1,1.8,virginica
diff --git a/data/iss.jpg b/data/iss.jpg
new file mode 100644
index 0000000..f05ac4f
Binary files /dev/null and b/data/iss.jpg differ
diff --git a/data/sms.wav b/data/sms.wav
new file mode 100644
index 0000000..0827dc5
Binary files /dev/null and b/data/sms.wav differ
diff --git a/data/test.csv b/data/test.csv
new file mode 100644
index 0000000..c841075
--- /dev/null
+++ b/data/test.csv
@@ -0,0 +1,301 @@
+x,y
+77,79.77515201
+21,23.17727887
+22,25.60926156
+20,17.85738813
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diff --git a/data/titanic-train.csv b/data/titanic-train.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/data/titanic-train.csv
@@ -0,0 +1,892 @@
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+826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
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+837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
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+844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
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+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
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+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
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+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
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diff --git a/data/us_retail_sales.csv b/data/us_retail_sales.csv
new file mode 100644
index 0000000..fefc768
--- /dev/null
+++ b/data/us_retail_sales.csv
@@ -0,0 +1 @@
+YEAR,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC
1992,164083,164260,163747,164759,165617,166098,167305,167797,169407,170681,171025,172995
1993,175078,173770,172328,176766,178445,178201,180759,180692,181800,182910,184746,186339
1994,185128,188077,191588,191632,190940,193196,193763,196157,197754,199579,199723,200670
1995,201583,198383,200230,201048,202993,205507,204959,206529,206978,206157,208661,210434
1996,208731,212011,213855,214644,216304,216059,216374,216355,219240,221039,220989,221898
1997,223524,225409,226136,224588,222906,226048,228738,229317,230284,229822,230486,231197
1998,231605,231664,233043,235976,237055,238958,237423,236412,238542,242531,244307,246577
1999,246891,249510,250657,252418,254738,255472,257441,260253,261352,261825,264883,269876
2000,268091,272020,275214,271004,271418,273440,272638,272943,277523,276973,275923,275736
2001,278916,278799,276468,280804,281540,280399,279522,281423,276095,294613,286960,283708
2002,283577,285061,284263,288820,284994,287401,290427,292582,288434,289634,291475,293819
2003,295294,291178,296347,295643,296395,299662,302788,307745,305916,304824,308551,307362
2004,309225,311427,316935,313531,318962,314971,318532,318945,324607,326680,327837,331877
2005,329187,332847,332517,337608,334551,344034,346715,342950,343711,344390,347047,347441
2006,357626,354614,356357,357320,356423,357763,359217,360497,358468,358435,359256,364300
2007,363462,364090,367714,366065,370887,367933,369472,370982,373138,375048,378371,375349
2008,375303,371661,371961,373877,375790,376584,374774,372091,366167,352900,339942,332065
2009,336977,335765,330219,331314,334290,339532,340286,346705,338356,341590,344911,346258
2010,346349,346972,354625,357086,354430,353830,354536,356560,359440,363855,367352,369299
2011,372041,375003,378472,380284,380398,383081,382966,383541,387313,390302,391478,391695
2012,395280,400181,401830,400095,399463,395723,397516,401937,405707,405598,407327,409468
2013,412207,416783,412514,413391,414207,415989,417962,417101,417682,419686,421056,422891
2014,419195,424657,429707,433082,434161,435587,435429,438598,437932,439829,441445,437996
2015,435881,433579,440463,440355,444400,444747,447112,447738,447641,446489,448724,450831
2016,446757,448895,447757,453397,454135,457409,457849,457722,462284,465321,466028,470616
2017,473104,471865,470844,,,,,,,,,
\ No newline at end of file
diff --git a/data/user_visit_duration.csv b/data/user_visit_duration.csv
new file mode 100644
index 0000000..02ac335
--- /dev/null
+++ b/data/user_visit_duration.csv
@@ -0,0 +1,101 @@
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diff --git a/data/weight-height.csv b/data/weight-height.csv
new file mode 100644
index 0000000..361c9b5
--- /dev/null
+++ b/data/weight-height.csv
@@ -0,0 +1,10001 @@
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diff --git a/data/wines.csv b/data/wines.csv
new file mode 100644
index 0000000..8f03f53
--- /dev/null
+++ b/data/wines.csv
@@ -0,0 +1,179 @@
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+3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
+3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
+3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
+3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
+3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
+3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
+3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
+3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
+3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
+3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
+3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
+3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
+3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
+3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
+3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
+3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
+3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
+3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
+3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
+3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
+3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..f5aa111
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,21 @@
+name: ztdl
+channels:
+- defaults
+dependencies:
+- python=3.7.*
+- bz2file==0.98
+- cython==0.29.*
+- pip==21.0.*
+- numpy==1.19.*
+- jupyter==1.0.*
+- matplotlib==3.3.*
+- setuptools==52.0.*
+- scikit-learn==0.24.*
+- scipy==1.6.*
+- pandas==1.2.*
+- pillow==8.2.*
+- seaborn==0.11.*
+- pytest==6.2.*
+- twisted==21.2.*
+- pip:
+  - tensorflow==2.5.*
diff --git a/exercises/First Deep Learning Model commented.ipynb b/exercises/First Deep Learning Model commented.ipynb
new file mode 100644
index 0000000..d6ca278
--- /dev/null
+++ b/exercises/First Deep Learning Model commented.ipynb	
@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Deep Learning Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # import the numpy library and assign the name np to it\n",
+    "%matplotlib inline # magic function that sets the backend of matplotlib to the inline backend\n",
+    "import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_circles # import the make_circles module from the sklearn.datasets module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_circles(n_samples=1000,\n",
+    "                    noise=0.1,\n",
+    "                    factor=0.2,\n",
+    "                    random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(4, input_shape=(2,), activation='tanh'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X, y, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hticks = np.linspace(-1.5, 1.5, 101)\n",
+    "vticks = np.linspace(-1.5, 1.5, 101)\n",
+    "aa, bb = np.meshgrid(hticks, vticks)\n",
+    "ab = np.c_[aa.ravel(), bb.ravel()]\n",
+    "c = model.predict(ab)\n",
+    "cc = c.reshape(aa.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercises/Jupyter notebook CVML.ipynb b/exercises/Jupyter notebook CVML.ipynb
new file mode 100644
index 0000000..ef4940d
--- /dev/null
+++ b/exercises/Jupyter notebook CVML.ipynb	
@@ -0,0 +1,2604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ab021475",
+   "metadata": {},
+   "source": [
+    "## Jupyter notebook Lars Rook & Sem van der Hoeven"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffa039c8",
+   "metadata": {},
+   "source": [
+    "# Week 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "239c1156",
+   "metadata": {},
+   "source": [
+    "## 4.1: ZTDL 1 - First Deep Learning Model\n",
+    "We hebben bij elk code blok comments gezet die uitleggen wat de code doet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "155b55bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # import the numpy library and assign the name np to it\n",
+    "\n",
+    "# magic function that sets the backend of matplotlib to the inline backend\n",
+    "# source: https://stackoverflow.com/questions/43027980/purpose-of-matplotlib-inline\n",
+    "%matplotlib inline \n",
+    "import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "feeaffc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_circles # import the make_circles module from the sklearn.datasets module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c1db0180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "roept de make_circles functie aan van de sklearn.datasets module\n",
+    "de make_circles functie maakt een cirkel met een kleinere cirkel hier binnen in.\n",
+    "- de n_samples staat voor hoeveel points gegenereerd moeten worden.\n",
+    "- de noise variabele staat voor hoeveel noise eraan toegeoegd moet worden\n",
+    "- de factor staat voor de schaling tussen de binnenste en buitenste cirkel\n",
+    "- de random_state variabele wordt gebruikt voor het genereren van een random nummer die gebruikt wordt voor het shufflen van de dataset en de noise.\n",
+    "\"\"\"\n",
+    "\n",
+    "X, y = make_circles(n_samples=1000,\n",
+    "                    noise=0.1,\n",
+    "                    factor=0.2,\n",
+    "                    random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "46267e93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.24265541,  0.0383196 ],\n",
+       "       [ 0.04433036, -0.05667334],\n",
+       "       [-0.78677748, -0.75718576],\n",
+       "       ...,\n",
+       "       [ 0.0161236 , -0.00548034],\n",
+       "       [ 0.20624715,  0.09769677],\n",
+       "       [-0.19186631,  0.08916672]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# X bevat de gegenereerde samples van de make_circles methode.\n",
+    "\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "34c5864c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# aangezien x de array of shape [n_samples, 2] bevat, geeft dit de gegeven aantal samples en 2.\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa762402",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Blue circles and Red crosses')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Hier wordt met matplotlib een grafiek van de samples getekend.\n",
+    "allereerst wordt er een figuur aangemaakt, daarna worden de waarden die zijn gegenereerd getekend op deze figuur.\n",
+    "Hierna worden de limits van de x en y assen gezet.\n",
+    "Daarna wordt de legenda aangemaakt, hierbij zijn de bolletjes een 0 en de kruisjes een 1.\n",
+    "Als laatst wordt de titel van de figuur aangemaakt.\n",
+    "\"\"\"\n",
+    "\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9efb32c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# importeer modules van tensorflow\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43c0a1d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Maak een sequentieel model aan\n",
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "585aa0e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# voeg een laag aan het model toe met 4 output neurons en 2 input neurons. Deze laag gebruikt de hyperbolic tangent activation functie.\n",
+    "model.add(Dense(4, input_shape=(2,), activation='tanh'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "84e68325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# voeg nog een laag toe met 1 output neuron. Deze laag gebruikt de Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x))\n",
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fef8e12a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "compile configureert het model voor het trainen.\n",
+    "- als optimizer wordt de gradient descent optimizer gebruikt met een learning rate van 0.5\n",
+    "- als loss wordt binary cross entropy gebruikt. \n",
+    "- bij de metrics parameter wordt een lijst van attributen gezet waarop het model wordt geëvalueerd. In dit geval is het alleen de nauwkeurigheid\n",
+    "\"\"\" \n",
+    "model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0e1bcf7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.6605 - accuracy: 0.6200\n",
+      "Epoch 2/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.5513 - accuracy: 0.8240\n",
+      "Epoch 3/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.9510\n",
+      "Epoch 4/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.2894 - accuracy: 0.9940\n",
+      "Epoch 5/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.2142 - accuracy: 1.0000\n",
+      "Epoch 6/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1680 - accuracy: 1.0000\n",
+      "Epoch 7/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1372 - accuracy: 1.0000\n",
+      "Epoch 8/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.1153 - accuracy: 1.0000\n",
+      "Epoch 9/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0988 - accuracy: 1.0000\n",
+      "Epoch 10/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0865 - accuracy: 1.0000\n",
+      "Epoch 11/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0765 - accuracy: 1.0000\n",
+      "Epoch 12/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0686 - accuracy: 1.0000\n",
+      "Epoch 13/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0622 - accuracy: 1.0000\n",
+      "Epoch 14/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0567 - accuracy: 1.0000\n",
+      "Epoch 15/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0519 - accuracy: 1.0000\n",
+      "Epoch 16/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0482 - accuracy: 1.0000\n",
+      "Epoch 17/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0447 - accuracy: 1.0000\n",
+      "Epoch 18/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0418 - accuracy: 0.9990\n",
+      "Epoch 19/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0392 - accuracy: 0.9990\n",
+      "Epoch 20/20\n",
+      "32/32 [==============================] - 0s 1ms/step - loss: 0.0369 - accuracy: 0.9990\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x26d8d1e0588>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "fit traint het model voor het aantal gegeven epochs.\n",
+    "- X staat voor de input samples.\n",
+    "- y staat voor de target data (tensors)\n",
+    "- epochs staat voor hoe vaak het model getrained wordt.\n",
+    "\"\"\"\n",
+    "model.fit(X, y, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "20f9bb50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "met de linspace functies worden nummers gegenereerd over een gelijk interval.\n",
+    "met de meshgrid functies worden coordinate matrices gemaakt van coordinate vectors.\n",
+    "met de c_ functie wordt een matrix gemaakt van de genenereerde arrays.\n",
+    "met de predict functie worden output predictions gegenereerd voor de input samples.\n",
+    "met de reshape functie wordt de shape aangepast naar die van de meshgrid.\n",
+    "\"\"\"\n",
+    "hticks = np.linspace(-1.5, 1.5, 101)\n",
+    "vticks = np.linspace(-1.5, 1.5, 101)\n",
+    "aa, bb = np.meshgrid(hticks, vticks)\n",
+    "ab = np.c_[aa.ravel(), bb.ravel()]\n",
+    "c = model.predict(ab)\n",
+    "cc = c.reshape(aa.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a2e4f082",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.5 , -1.5 ],\n",
+       "       [-1.47, -1.5 ],\n",
+       "       [-1.44, -1.5 ],\n",
+       "       ...,\n",
+       "       [ 1.44,  1.5 ],\n",
+       "       [ 1.47,  1.5 ],\n",
+       "       [ 1.5 ,  1.5 ]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1e5b54db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Blue circles and Red crosses')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Hier wordt met matplotlib een grafiek van de samples getekend.\n",
+    "allereerst wordt er een figuur aangemaakt\n",
+    "Daarna wordt de contour getekend aan de hand van de output van het model.\n",
+    "Daarna worden de waarden die zijn gegenereerd getekend op deze figuur.\n",
+    "Hierna worden de limits van de x en y assen gezet.\n",
+    "Daarna wordt de legenda aangemaakt, hierbij zijn de bolletjes een 0 en de kruisjes een 1.\n",
+    "Als laatst wordt de titel van de figuur aangemaakt.\n",
+    "\"\"\"\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
+    "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
+    "plt.xlim(-1.5, 1.5)\n",
+    "plt.ylim(-1.5, 1.5)\n",
+    "plt.legend(['0', '1'])\n",
+    "plt.title(\"Blue circles and Red crosses\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9267f151",
+   "metadata": {},
+   "source": [
+    "## 4.2 : ZTDL 2 - Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20cea62e",
+   "metadata": {},
+   "source": [
+    "a. we hebben allebei het notebook bestudeerd.\n",
+    "b. we hebben een spreadsheet gevonden over heart attack analysis and predictions.\n",
+    "c. Hieronder zijn een aantal technieken en plots te zien die wij op de data hebben uitgevoerd. Om het beter te begrijpen hebben wij allebei samen de technieken en plots uitgevoerd."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaab705e",
+   "metadata": {},
+   "source": [
+    "### Standaard info\n",
+    "We hebben eerst de standaard gegevens van de dataset verkend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4deee696",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6b792675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/heart.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "81faf37f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6417b5c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>63</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>145</td>\n",
+       "      <td>233</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>130</td>\n",
+       "      <td>250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>130</td>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>120</td>\n",
+       "      <td>236</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>354</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>163</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
+       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
+       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
+       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
+       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
+       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
+       "\n",
+       "   caa  thall  output  \n",
+       "0    0      1       1  \n",
+       "1    0      2       1  \n",
+       "2    0      2       1  \n",
+       "3    0      2       1  \n",
+       "4    0      2       1  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "202237c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 303 entries, 0 to 302\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   age       303 non-null    int64  \n",
+      " 1   sex       303 non-null    int64  \n",
+      " 2   cp        303 non-null    int64  \n",
+      " 3   trtbps    303 non-null    int64  \n",
+      " 4   chol      303 non-null    int64  \n",
+      " 5   fbs       303 non-null    int64  \n",
+      " 6   restecg   303 non-null    int64  \n",
+      " 7   thalachh  303 non-null    int64  \n",
+      " 8   exng      303 non-null    int64  \n",
+      " 9   oldpeak   303 non-null    float64\n",
+      " 10  slp       303 non-null    int64  \n",
+      " 11  caa       303 non-null    int64  \n",
+      " 12  thall     303 non-null    int64  \n",
+      " 13  output    303 non-null    int64  \n",
+      "dtypes: float64(1), int64(13)\n",
+      "memory usage: 33.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9a94370a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>54.366337</td>\n",
+       "      <td>0.683168</td>\n",
+       "      <td>0.966997</td>\n",
+       "      <td>131.623762</td>\n",
+       "      <td>246.264026</td>\n",
+       "      <td>0.148515</td>\n",
+       "      <td>0.528053</td>\n",
+       "      <td>149.646865</td>\n",
+       "      <td>0.326733</td>\n",
+       "      <td>1.039604</td>\n",
+       "      <td>1.399340</td>\n",
+       "      <td>0.729373</td>\n",
+       "      <td>2.313531</td>\n",
+       "      <td>0.544554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9.082101</td>\n",
+       "      <td>0.466011</td>\n",
+       "      <td>1.032052</td>\n",
+       "      <td>17.538143</td>\n",
+       "      <td>51.830751</td>\n",
+       "      <td>0.356198</td>\n",
+       "      <td>0.525860</td>\n",
+       "      <td>22.905161</td>\n",
+       "      <td>0.469794</td>\n",
+       "      <td>1.161075</td>\n",
+       "      <td>0.616226</td>\n",
+       "      <td>1.022606</td>\n",
+       "      <td>0.612277</td>\n",
+       "      <td>0.498835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>126.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>47.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>211.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>133.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>130.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>153.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>274.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>166.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>564.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>202.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              age         sex          cp      trtbps        chol         fbs  \\\n",
+       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
+       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
+       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
+       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
+       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
+       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
+       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
+       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
+       "\n",
+       "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
+       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
+       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
+       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
+       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
+       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
+       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
+       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
+       "\n",
+       "            thall      output  \n",
+       "count  303.000000  303.000000  \n",
+       "mean     2.313531    0.544554  \n",
+       "std      0.612277    0.498835  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      2.000000    0.000000  \n",
+       "50%      2.000000    1.000000  \n",
+       "75%      3.000000    1.000000  \n",
+       "max      3.000000    1.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "345b68bd",
+   "metadata": {},
+   "source": [
+    "### indexing\n",
+    "We zullen nu verschillende items in de dataset indexeren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ce906842",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age          54.0\n",
+       "sex           1.0\n",
+       "cp            0.0\n",
+       "trtbps      140.0\n",
+       "chol        239.0\n",
+       "fbs           0.0\n",
+       "restecg       1.0\n",
+       "thalachh    160.0\n",
+       "exng          0.0\n",
+       "oldpeak       1.2\n",
+       "slp           2.0\n",
+       "caa           0.0\n",
+       "thall         2.0\n",
+       "output        1.0\n",
+       "Name: 10, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# het 10de element ophalen uit de dataset\n",
+    "df.iloc[10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5f8d9afe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>oldpeak</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>63</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>52</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  sex  oldpeak\n",
+       "0   63    1      2.3\n",
+       "1   37    1      3.5\n",
+       "2   41    0      1.4\n",
+       "3   56    1      0.8\n",
+       "4   57    0      0.6\n",
+       "5   57    1      0.4\n",
+       "6   56    0      1.3\n",
+       "7   44    1      0.0\n",
+       "8   52    1      0.5"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# de age, sex en oldpeak van de eerste 9 elementen ophalen\n",
+    "df.loc[0:8,['age','sex','oldpeak']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ce4a5f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    1\n",
+       "2    0\n",
+       "3    1\n",
+       "4    0\n",
+       "Name: sex, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# de head opvragen van de elementen met het sex attribuut\n",
+    "df['sex'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d129b322",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# unieke element van sex ophalen\n",
+    "df['sex'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "37d36915",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>130</td>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>202</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>118</td>\n",
+       "      <td>182</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>118</td>\n",
+       "      <td>210</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>192</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>126</td>\n",
+       "      <td>282</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>156</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>138</td>\n",
+       "      <td>183</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>182</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>265</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>120</td>\n",
+       "      <td>269</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>140</td>\n",
+       "      <td>197</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>116</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>77</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>304</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>162</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>303 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
+       "72    29    1   1     130   204    0        0       202     0      0.0    2   \n",
+       "58    34    1   3     118   182    0        0       174     0      0.0    2   \n",
+       "125   34    0   1     118   210    0        1       192     0      0.7    2   \n",
+       "239   35    1   0     126   282    0        0       156     1      0.0    2   \n",
+       "65    35    0   0     138   183    0        1       182     0      1.4    2   \n",
+       "..   ...  ...  ..     ...   ...  ...      ...       ...   ...      ...  ...   \n",
+       "60    71    0   2     110   265    1        0       130     0      0.0    2   \n",
+       "151   71    0   0     112   149    0        1       125     0      1.6    1   \n",
+       "129   74    0   1     120   269    0        0       121     1      0.2    2   \n",
+       "144   76    0   2     140   197    0        2       116     0      1.1    1   \n",
+       "238   77    1   0     125   304    0        0       162     1      0.0    2   \n",
+       "\n",
+       "     caa  thall  output  \n",
+       "72     0      2       1  \n",
+       "58     0      2       1  \n",
+       "125    0      2       1  \n",
+       "239    0      3       0  \n",
+       "65     0      2       1  \n",
+       "..   ...    ...     ...  \n",
+       "60     1      2       1  \n",
+       "151    0      2       1  \n",
+       "129    1      2       1  \n",
+       "144    0      2       1  \n",
+       "238    3      2       0  \n",
+       "\n",
+       "[303 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sorteer de values van attribuut age op optellende manier\n",
+    "df.sort_values('age', ascending = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c379d785",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'age chol relation'}>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfplot = df[['age','chol']]\n",
+    "dfplot.plot(title='age chol relation')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "601da936",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True     207\n",
+       "False     96\n",
+       "Name: sex, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amountMale = df['sex'] > 0 # assuming male = 1\n",
+    "piecounts = amountMale.value_counts()\n",
+    "piecounts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a128aabd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "piecounts.plot(kind='pie',\n",
+    "               figsize=(5, 5),\n",
+    "               explode=[0, 0.12],\n",
+    "               labels=['male', 'female'],\n",
+    "               autopct='%1.1f%%',\n",
+    "               shadow=True,\n",
+    "               startangle=60,\n",
+    "               fontsize=16);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95e144df",
+   "metadata": {},
+   "source": [
+    "## 4.3 : ZTDL 3 – Machine Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79972227",
+   "metadata": {},
+   "source": [
+    "### Linear regression\n",
+    "Deze code blokken zijn nodig als setup voor de linear regression met keras\n",
+    "We hebben gekozen voor een dataset die gebruikt kan worden voor linear regression. De dataset heeft geen bepaalde betekenis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09ee5be2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "37389405",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  read the csv file into the df variable\n",
+    "df = pd.read_csv('../data/test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a7ea6eb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>77</td>\n",
+       "      <td>79.775152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21</td>\n",
+       "      <td>23.177279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>25.609262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20</td>\n",
+       "      <td>17.857388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>36</td>\n",
+       "      <td>41.849864</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    x          y\n",
+       "0  77  79.775152\n",
+       "1  21  23.177279\n",
+       "2  22  25.609262\n",
+       "3  20  17.857388\n",
+       "4  36  41.849864"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out what is in the first few lines\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "952802ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'x and y in the dataset'}, xlabel='x', ylabel='y'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the points in the dataset\n",
+    "df.plot(kind='scatter',\n",
+    "        x='x',\n",
+    "        y='y',\n",
+    "        title='x and y in the dataset')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8e9d0f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x251e44ef948>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the points again\n",
+    "df.plot(kind='scatter',\n",
+    "        x='x',\n",
+    "        y='y',\n",
+    "        title='y and x in the data set')\n",
+    "\n",
+    "# Here we're plotting the red line 'by hand' with fixed values\n",
+    "# We'll try to learn this line with an algorithm below\n",
+    "plt.plot([55, 78], [75, 250], color='red', linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bf4cd5b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a method to calculate a point for a line given the input values\n",
+    "def line(x, w=0, b=0):\n",
+    "    return x * w + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b4842e99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate evenly spaced numbers \n",
+    "x = np.linspace(55, 80, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "479278e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([55.        , 55.25252525, 55.50505051, 55.75757576, 56.01010101,\n",
+       "       56.26262626, 56.51515152, 56.76767677, 57.02020202, 57.27272727,\n",
+       "       57.52525253, 57.77777778, 58.03030303, 58.28282828, 58.53535354,\n",
+       "       58.78787879, 59.04040404, 59.29292929, 59.54545455, 59.7979798 ,\n",
+       "       60.05050505, 60.3030303 , 60.55555556, 60.80808081, 61.06060606,\n",
+       "       61.31313131, 61.56565657, 61.81818182, 62.07070707, 62.32323232,\n",
+       "       62.57575758, 62.82828283, 63.08080808, 63.33333333, 63.58585859,\n",
+       "       63.83838384, 64.09090909, 64.34343434, 64.5959596 , 64.84848485,\n",
+       "       65.1010101 , 65.35353535, 65.60606061, 65.85858586, 66.11111111,\n",
+       "       66.36363636, 66.61616162, 66.86868687, 67.12121212, 67.37373737,\n",
+       "       67.62626263, 67.87878788, 68.13131313, 68.38383838, 68.63636364,\n",
+       "       68.88888889, 69.14141414, 69.39393939, 69.64646465, 69.8989899 ,\n",
+       "       70.15151515, 70.4040404 , 70.65656566, 70.90909091, 71.16161616,\n",
+       "       71.41414141, 71.66666667, 71.91919192, 72.17171717, 72.42424242,\n",
+       "       72.67676768, 72.92929293, 73.18181818, 73.43434343, 73.68686869,\n",
+       "       73.93939394, 74.19191919, 74.44444444, 74.6969697 , 74.94949495,\n",
+       "       75.2020202 , 75.45454545, 75.70707071, 75.95959596, 76.21212121,\n",
+       "       76.46464646, 76.71717172, 76.96969697, 77.22222222, 77.47474747,\n",
+       "       77.72727273, 77.97979798, 78.23232323, 78.48484848, 78.73737374,\n",
+       "       78.98989899, 79.24242424, 79.49494949, 79.74747475, 80.        ])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "56cd60cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate line points\n",
+    "yhat = line(x, w=0, b=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bd73d23f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yhat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7af98bd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x251e4561388>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw the points again, now with the line\n",
+    "df.plot(kind='scatter',\n",
+    "        x='x',\n",
+    "        y='y',\n",
+    "        title='y en x in the data set')\n",
+    "plt.plot(x, yhat, color='red', linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "077d7978",
+   "metadata": {},
+   "source": [
+    "### Cost function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "62838ac9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the mean squared error given the input parameters.\n",
+    "def mean_squared_error(y_true, y_pred):\n",
+    "    s = (y_true - y_pred)**2\n",
+    "    return s.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4bea9b4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the x and y values from the dataset\n",
+    "X = df[['x']].values\n",
+    "y_true = df['y'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "392f50b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 79.77515201,  23.17727887,  25.60926156,  17.85738813,\n",
+       "        41.84986439,   9.80523488,  58.87465933,  97.61793701,\n",
+       "        18.39512747,   8.74674765,   2.81141583,  17.09537241,\n",
+       "        95.14907176,  61.38800663,  40.24701716,  14.82248589,\n",
+       "        66.95806869,  16.63507984,  90.65513736,  77.22982636,\n",
+       "        92.11906278,  46.91387709,  89.82634442,  21.71380347,\n",
+       "        97.41206981,  57.01631363,  78.31056542,  19.1315097 ,\n",
+       "        93.03483388,  26.59112396,  97.55155344,  31.43524822,\n",
+       "        35.12724777,  78.61042432,  33.07112825,  51.69967172,\n",
+       "        53.62235225,  69.46306072,  27.42497237,  36.34644189,\n",
+       "        95.06140858,  68.16724757,  50.96155532,  78.04237454,\n",
+       "         5.60766487,  36.11334779,  67.2352155 ,  65.01324035,\n",
+       "        38.14753871,  34.31141446,  95.28503937,  87.84749912,\n",
+       "        54.08170635,  31.93063515,  59.61247085,  -1.04011421,\n",
+       "        47.49374765,  62.60089773,  70.9146434 ,  56.14834113,\n",
+       "        14.05572877,  68.11367147,  75.59701346,  59.225745  ,\n",
+       "        85.45504157,  17.76197116,  38.68888682,  50.96343637,\n",
+       "        51.83503872,  17.0761107 ,  46.56141773,  10.34754461,\n",
+       "        77.91032969,  50.17008622,  13.25690647,  31.32274932,\n",
+       "        73.9308764 ,  74.45114379,  52.01932286,  83.68820499,\n",
+       "        70.3698748 ,  23.44479161,  49.83051801,  49.88226593,\n",
+       "        41.04525583,  33.37834391,  81.29750133, 105.5918375 ,\n",
+       "        56.82457013,  48.67252645,  67.02150613,  38.43076389,\n",
+       "        58.61466887,  89.12377509,  60.9105427 ,  13.83959878,\n",
+       "        16.89085185,  84.06676818,  70.34969772,  33.38474138,\n",
+       "        -1.63296825,  88.54475895,  17.44047622,  75.69298554,\n",
+       "        41.97607107,  12.59244741,   0.27530726,  98.13258005,\n",
+       "        87.45721555,  -2.34473854,  39.3294153 ,  16.68715211,\n",
+       "        96.58888601,  97.70342201,  67.01715955,  25.63476257,\n",
+       "        13.41310757,  95.15647284,   9.74416426,  -3.46788379,\n",
+       "        62.82816355,  97.27405461,  95.58017185,   7.46850184,\n",
+       "        45.44599591,  46.69013968,  74.4993599 ,  21.63500655,\n",
+       "        91.59548851,  26.49487961,  67.38654703,  74.25362837,\n",
+       "        12.07991648,  21.32273728,  29.31770045,  26.48713683,\n",
+       "        68.94699774,  59.10598995,  64.37521087,  60.20758349,\n",
+       "        70.34329706,  97.1082562 ,  75.7584178 ,  10.80462727,\n",
+       "        12.11219941,  63.28312382,  98.03017721,  63.19354354,\n",
+       "        34.8534823 ,  -2.81991397,  59.8313966 ,  29.38505024,\n",
+       "        97.00148372,  85.18657275,  61.74063192,  18.84798163,\n",
+       "        78.79008525,  95.12400481,  30.48881287,  10.41468095,\n",
+       "        38.98317436,  46.11021062,  52.45103628,  21.16523945,\n",
+       "        52.28620611,  44.18863945,  97.13832018,  67.22008001,\n",
+       "        18.98322306,  24.3884599 ,  79.44769523,  40.03504862,\n",
+       "        53.32005764,  54.55446979,  -2.7611826 ,  37.80182795,\n",
+       "        57.48741435,  36.06292994,  49.83538167,  74.68953276,\n",
+       "        14.86159401, 101.0697879 ,  99.43577876,  91.69240746,\n",
+       "        34.12473248,   6.07939007,  59.07247174,  56.43046022,\n",
+       "        30.49412933,  48.35172635,  89.73153611,  72.86282528,\n",
+       "        80.97144285,  91.36566374,  60.07137496,  99.87382707,\n",
+       "         8.65571417,  69.39858505,  19.38780134,  53.11628433,\n",
+       "        78.39683006,  25.75612514,  75.07484683,  92.88772282,\n",
+       "        69.45498498,  13.12109842,  48.09843134,  79.3142548 ,\n",
+       "        68.48820749,  73.2300846 ,  24.68362712,  41.90368917,\n",
+       "        62.22635684,  45.96396877,  23.52647153,  51.80035866,\n",
+       "        51.10774273,  95.79747345,   9.24113898,   7.64652976,\n",
+       "         9.28169975, 103.5266162 ,  47.41006725,  42.03835773,\n",
+       "        96.11982476,  38.05766408, 105.4503788 ,  88.80306911,\n",
+       "        15.49301141,  12.42624606,  40.00709598,   5.6340309 ,\n",
+       "        87.36938931,  89.73951993,  66.61499643,  72.9138853 ,\n",
+       "        57.19103506,  11.21710477,   0.67607675,  28.15668543,\n",
+       "        95.3958003 ,  52.05490703,  59.70864577,  36.79224762,\n",
+       "        37.08457698,  24.18437976,  67.28725332,  82.870594  ,\n",
+       "        89.899991  ,  36.94173178,  19.87562242,  90.71481654,\n",
+       "        61.09367762,  60.11134958,  64.83296316,  81.40381769,\n",
+       "        92.40217686,   2.57662538,  63.80768172,  38.67780759,\n",
+       "        16.82839701,  99.78687252,  44.68913433,  71.00377824,\n",
+       "        51.57326718,  19.87846479,  79.50341495,  34.58876491,\n",
+       "        55.7383467 ,  68.19721905,  55.81628509,   9.3914168 ,\n",
+       "        56.01448111,  77.9969477 ,  55.37049953,  11.89457829,\n",
+       "        94.79081712,  25.69041546,  53.52042319,  18.31396758,\n",
+       "        21.42637785,  30.41303282,  67.68142149,  17.0854783 ,\n",
+       "        60.91792707,  14.99514319,  16.74923937,  41.46923883,\n",
+       "        42.84526108,  59.12912974,  91.30863673,   8.67333636,\n",
+       "        39.31485292,   5.3136862 ,   5.40522052,  68.5458879 ,\n",
+       "        47.33487629,  54.09063686,  63.29717058,  52.45946688])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0806d238",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert the points of the y values to line points\n",
+    "y_pred = line(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "aa0ef0f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "       [0],\n",
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+       "       [0],\n",
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+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0],\n",
+       "       [0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "55ff0a77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3464.291087139079"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate the mean square error for the points\n",
+    "mean_squared_error(y_true, y_pred.ravel())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60e5104",
+   "metadata": {},
+   "source": [
+    "### Linear regression with keras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ccde02ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam, SGD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3c2b554a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4da8d3b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a model with 1 layer, 1 output node and 1 input node\n",
+    "model.add(Dense(1, input_shape=(1,)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "afbf17ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense (Dense)                (None, 1)                 2         \n",
+      "=================================================================\n",
+      "Total params: 2\n",
+      "Trainable params: 2\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "35dda627",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the model ready for training\n",
+    "model.compile(Adam(learning_rate=0.8), 'mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fe11b9d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 276.5771\n",
+      "Epoch 2/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 70.1189\n",
+      "Epoch 3/40\n",
+      "10/10 [==============================] - 0s 893us/step - loss: 38.0515\n",
+      "Epoch 4/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 27.3967\n",
+      "Epoch 5/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 25.7747\n",
+      "Epoch 6/40\n",
+      "10/10 [==============================] - 0s 777us/step - loss: 20.8342\n",
+      "Epoch 7/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 15.4722\n",
+      "Epoch 8/40\n",
+      "10/10 [==============================] - 0s 997us/step - loss: 9.9356\n",
+      "Epoch 9/40\n",
+      "10/10 [==============================] - 0s 778us/step - loss: 9.9688\n",
+      "Epoch 10/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.1344\n",
+      "Epoch 11/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.7054\n",
+      "Epoch 12/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 9.7593\n",
+      "Epoch 13/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.4868\n",
+      "Epoch 14/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.8315\n",
+      "Epoch 15/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 10.3213\n",
+      "Epoch 16/40\n",
+      "10/10 [==============================] - 0s 999us/step - loss: 17.9203\n",
+      "Epoch 17/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 14.1653\n",
+      "Epoch 18/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 18.5441\n",
+      "Epoch 19/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 15.2991\n",
+      "Epoch 20/40\n",
+      "10/10 [==============================] - 0s 778us/step - loss: 10.4892\n",
+      "Epoch 21/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 9.3215\n",
+      "Epoch 22/40\n",
+      "10/10 [==============================] - ETA: 0s - loss: 9.491 - 0s 1ms/step - loss: 10.7091\n",
+      "Epoch 23/40\n",
+      "10/10 [==============================] - 0s 885us/step - loss: 10.4797\n",
+      "Epoch 24/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 11.5123\n",
+      "Epoch 25/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 13.3325\n",
+      "Epoch 26/40\n",
+      "10/10 [==============================] - 0s 775us/step - loss: 13.3366\n",
+      "Epoch 27/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 16.0355\n",
+      "Epoch 28/40\n",
+      "10/10 [==============================] - ETA: 0s - loss: 24.20 - 0s 1ms/step - loss: 14.7609\n",
+      "Epoch 29/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 10.3856\n",
+      "Epoch 30/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.1707\n",
+      "Epoch 31/40\n",
+      "10/10 [==============================] - 0s 778us/step - loss: 9.4444\n",
+      "Epoch 32/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.1405\n",
+      "Epoch 33/40\n",
+      "10/10 [==============================] - 0s 890us/step - loss: 13.1112\n",
+      "Epoch 34/40\n",
+      "10/10 [==============================] - 0s 778us/step - loss: 10.2187\n",
+      "Epoch 35/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 10.7566\n",
+      "Epoch 36/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 11.3941\n",
+      "Epoch 37/40\n",
+      "10/10 [==============================] - 0s 889us/step - loss: 15.5596\n",
+      "Epoch 38/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 15.3198\n",
+      "Epoch 39/40\n",
+      "10/10 [==============================] - 0s 1ms/step - loss: 13.1469\n",
+      "Epoch 40/40\n",
+      "10/10 [==============================] - 0s 888us/step - loss: 9.4861\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x251ea00efc8>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# start training the model for 40 epochs\n",
+    "model.fit(X, y_true, epochs=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a22c9fb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9787ea0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x251f2e13148>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='scatter',\n",
+    "        x='x',\n",
+    "        y='y',\n",
+    "        title='x and y in the dataset, with prediction')\n",
+    "plt.plot(X, y_pred, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ccaf0068",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W, B = model.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5e966641",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.030289]], dtype=float32)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d7d8eb87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44838893], dtype=float32)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18ef5e31",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/exercises/Untitled.ipynb b/exercises/Untitled.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/exercises/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/solutions/2 Data exploration Exercises Solution.ipynb b/solutions/2 Data exploration Exercises Solution.ipynb
new file mode 100644
index 0000000..3e5fab8
--- /dev/null
+++ b/solutions/2 Data exploration Exercises Solution.ipynb	
@@ -0,0 +1,404 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "- load the dataset: `../data/international-airline-passengers.csv`\n",
+    "- inspect it using the `.info()` and `.head()` commands\n",
+    "- use the function `pd.to_datetime()` to change the column type of 'Month' to a datatime type\n",
+    "- set the index of df to be a datetime index using the column 'Month' and the `df.set_index()` method\n",
+    "- choose the appropriate plot and display the data\n",
+    "- choose appropriate scale\n",
+    "- label the axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - load the dataset: ../data/international-airline-passengers.csv\n",
+    "df = pd.read_csv('../data/international-airline-passengers.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - inspect it using the .info() and .head() commands\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - use the function to_datetime() to change the column type of 'Month' to a datatime type\n",
+    "# - set the index of df to be a datetime index using the column 'Month' and tthe set_index() method\n",
+    "\n",
+    "df['Month'] = pd.to_datetime(df['Month'])\n",
+    "df = df.set_index('Month')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - choose the appropriate plot and display the data\n",
+    "# - choose appropriate scale\n",
+    "# - label the axes\n",
+    "\n",
+    "df.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "- load the dataset: `../data/weight-height.csv`\n",
+    "- inspect it\n",
+    "- plot it using a scatter plot with Weight as a function of Height\n",
+    "- plot the male and female populations with 2 different colors on a new scatter plot\n",
+    "- remember to label the axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - load the dataset: ../data/weight-height.csv\n",
+    "# - inspect it\n",
+    "df = pd.read_csv('../data/weight-height.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - plot it using a scatter plot with Weight as a function of Height\n",
+    "_ = df.plot(kind='scatter', x='Height', y='Weight');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - plot the male and female populations with 2 different colors on a new scatter plot\n",
+    "# - remember to label the axes\n",
+    "\n",
+    "# this can be done in several ways, showing 2 here:\n",
+    "males = df[df['Gender'] == 'Male']\n",
+    "females = df.query('Gender == \"Female\"')\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "males.plot(kind='scatter', x='Height', y='Weight',\n",
+    "           ax=ax, color='blue', alpha=0.3,\n",
+    "           title='Male & Female Populations')\n",
+    "\n",
+    "females.plot(kind='scatter', x='Height', y='Weight',\n",
+    "             ax=ax, color='red', alpha=0.3);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gendercolor'] = df['Gender'].map({'Male': 'blue', 'Female': 'red'})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind='scatter', \n",
+    "        x='Height',\n",
+    "        y='Weight',\n",
+    "        c=df['Gendercolor'],\n",
+    "        alpha=0.3,\n",
+    "        title='Male & Female Populations');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(males['Height'], males['Weight'], 'ob', \n",
+    "        females['Height'], females['Weight'], 'or', alpha=0.3)\n",
+    "plt.xlabel('Height')\n",
+    "plt.ylabel('Weight')\n",
+    "plt.title('Male & Female Populations');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Exercise 3\n",
+    "- plot the histogram of the heights for males and for females on the same plot\n",
+    "- use alpha to control transparency in the plot comand\n",
+    "- plot a vertical line at the mean of each population using `plt.axvline()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "males['Height'].plot(kind='hist',\n",
+    "                     bins=50,\n",
+    "                     range=(50, 80),\n",
+    "                     alpha=0.3,\n",
+    "                     color='blue')\n",
+    "\n",
+    "females['Height'].plot(kind='hist',\n",
+    "                       bins=50,\n",
+    "                       range=(50, 80),\n",
+    "                       alpha=0.3,\n",
+    "                       color='red')\n",
+    "\n",
+    "plt.title('Height distribution')\n",
+    "plt.legend([\"Males\", \"Females\"])\n",
+    "plt.xlabel(\"Heigth (in)\")\n",
+    "\n",
+    "\n",
+    "plt.axvline(males['Height'].mean(), color='blue', linewidth=2)\n",
+    "plt.axvline(females['Height'].mean(), color='red', linewidth=2);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "males['Height'].plot(kind='hist',\n",
+    "                     bins=200,\n",
+    "                     range=(50, 80),\n",
+    "                     alpha=0.3,\n",
+    "                     color='blue',\n",
+    "                     cumulative=True,\n",
+    "                     density=True)\n",
+    "\n",
+    "females['Height'].plot(kind='hist',\n",
+    "                       bins=200,\n",
+    "                       range=(50, 80),\n",
+    "                       alpha=0.3,\n",
+    "                       color='red',\n",
+    "                       cumulative=True,\n",
+    "                       density=True)\n",
+    "\n",
+    "plt.title('Height distribution')\n",
+    "plt.legend([\"Males\", \"Females\"])\n",
+    "plt.xlabel(\"Heigth (in)\")\n",
+    "\n",
+    "plt.axhline(0.8)\n",
+    "plt.axhline(0.5)\n",
+    "plt.axhline(0.2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "- plot the weights of the males and females using a box plot\n",
+    "- which one is easier to read?\n",
+    "- (remember to put in titles, axes and legends)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfpvt = df.pivot(columns = 'Gender', values = 'Weight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfpvt.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfpvt.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfpvt.plot(kind='box')\n",
+    "plt.title('Weight Box Plot')\n",
+    "plt.ylabel(\"Weight (lbs)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5\n",
+    "- load the dataset: `../data/titanic-train.csv`\n",
+    "- learn about scattermatrix here: http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
+    "- display the data using a scattermatrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/titanic-train.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.plotting import scatter_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = scatter_matrix(df.drop('PassengerId', axis=1), figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/solutions/3 Machine Learning Exercises Solution.ipynb b/solutions/3 Machine Learning Exercises Solution.ipynb
new file mode 100644
index 0000000..604e979
--- /dev/null
+++ b/solutions/3 Machine Learning Exercises Solution.ipynb	
@@ -0,0 +1,669 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Exercises Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "You've just been hired at a real estate investment firm and they would like you to build a model for pricing houses. You are given a dataset that contains data for house prices and a few features like number of bedrooms, size in square feet and age of the house. Let's see if you can build a model that is able to predict the price. In this exercise we extend what we have learned about linear regression to a dataset with more than one feature. Here are the steps to complete it:\n",
+    "\n",
+    "1. Load the dataset ../data/housing-data.csv\n",
+    "- plot the histograms for each feature\n",
+    "- create 2 variables called X and y: X shall be a matrix with 3 columns (sqft,bdrms,age) and y shall be a vector with 1 column (price)\n",
+    "- create a linear regression model in Keras with the appropriate number of inputs and output\n",
+    "- split the data into train and test with a 20% test size\n",
+    "- train the model on the training set and check its accuracy on training and test set\n",
+    "- how's your model doing? Is the loss growing smaller?\n",
+    "- try to improve your model with these experiments:\n",
+    "    - normalize the input features with one of the rescaling techniques mentioned above\n",
+    "    - use a different value for the learning rate of your model\n",
+    "    - use a different optimizer\n",
+    "- once you're satisfied with training, check the R2score on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset ../data/housing-data.csv\n",
+    "df = pd.read_csv('../data/housing-data.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the histograms for each feature\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "for i, feature in enumerate(df.columns):\n",
+    "    plt.subplot(1, 4, i+1)\n",
+    "    df[feature].plot(kind='hist', title=feature)\n",
+    "    plt.xlabel(feature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create 2 variables called X and y:\n",
+    "# X shall be a matrix with 3 columns (sqft,bdrms,age)\n",
+    "# and y shall be a vector with 1 column (price)\n",
+    "X = df[['sqft', 'bdrms', 'age']].values\n",
+    "y = df['price'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a linear regression model in Keras\n",
+    "# with the appropriate number of inputs and output\n",
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_shape=(3,)))\n",
+    "model.compile(Adam(learning_rate=0.8), 'mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the data into train and test with a 20% test size\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train the model on the training set and check its accuracy on training and test set\n",
+    "# how's your model doing? Is the loss growing smaller?\n",
+    "model.fit(X_train, y_train, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the R2score on training and test set (probably very bad)\n",
+    "\n",
+    "y_train_pred = model.predict(X_train)\n",
+    "y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
+    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# try to improve your model with these experiments:\n",
+    "#     - normalize the input features with one of the rescaling techniques mentioned above\n",
+    "#     - use a different value for the learning rate of your model\n",
+    "#     - use a different optimizer\n",
+    "df['sqft1000'] = df['sqft']/1000.0\n",
+    "df['age10'] = df['age']/10.0\n",
+    "df['price100k'] = df['price']/1e5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[['sqft1000', 'bdrms', 'age10']].values\n",
+    "y = df['price100k'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_dim=3))\n",
+    "model.compile(Adam(learning_rate=0.1), 'mean_squared_error')\n",
+    "model.fit(X_train, y_train, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# once you're satisfied with training, check the R2score on the test set\n",
+    "\n",
+    "y_train_pred = model.predict(X_train)\n",
+    "y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
+    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=40, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# once you're satisfied with training, check the R2score on the test set\n",
+    "\n",
+    "y_train_pred = model.predict(X_train)\n",
+    "y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred)))\n",
+    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_test_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "Your boss was extremely happy with your work on the housing price prediction model and decided to entrust you with a more challenging task. They've seen a lot of people leave the company recently and they would like to understand why that's happening. They have collected historical data on employees and they would like you to build a model that is able to predict which employee will leave next. The would like a model that is better than random guessing. They also prefer false negatives than false positives, in this first phase. Fields in the dataset include:\n",
+    "\n",
+    "- Employee satisfaction level\n",
+    "- Last evaluation\n",
+    "- Number of projects\n",
+    "- Average monthly hours\n",
+    "- Time spent at the company\n",
+    "- Whether they have had a work accident\n",
+    "- Whether they have had a promotion in the last 5 years\n",
+    "- Department\n",
+    "- Salary\n",
+    "- Whether the employee has left\n",
+    "\n",
+    "Your goal is to predict the binary outcome variable `left` using the rest of the data. Since the outcome is binary, this is a classification problem. Here are some things you may want to try out:\n",
+    "\n",
+    "1. load the dataset at ../data/HR_comma_sep.csv, inspect it with `.head()`, `.info()` and `.describe()`.\n",
+    "- Establish a benchmark: what would be your accuracy score if you predicted everyone stay?\n",
+    "- Check if any feature needs rescaling. You may plot a histogram of the feature to decide which rescaling method is more appropriate.\n",
+    "- convert the categorical features into binary dummy columns. You will then have to combine them with the numerical features using `pd.concat`.\n",
+    "- do the usual train/test split with a 20% test size\n",
+    "- play around with learning rate and optimizer\n",
+    "- check the confusion matrix, precision and recall\n",
+    "- check if you still get the same results if you use a 5-Fold cross validation on all the data\n",
+    "- Is the model good enough for your boss?\n",
+    "\n",
+    "As you will see in this exercise, the a logistic regression model is not good enough to help your boss. In the next chapter we will learn how to go beyond linear models.\n",
+    "\n",
+    "This dataset comes from https://www.kaggle.com/ludobenistant/hr-analytics/ and is released under [CC BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the dataset at ../data/HR_comma_sep.csv, inspect it with `.head()`, `.info()` and `.describe()`.\n",
+    "\n",
+    "df = pd.read_csv('../data/HR_comma_sep.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Establish a benchmark: what would be your accuracy score if you predicted everyone stay?\n",
+    "\n",
+    "df.left.value_counts() / len(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predicting 0 all the time would yield an accuracy of 76%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if any feature needs rescaling.\n",
+    "# You may plot a histogram of the feature to decide which rescaling method is more appropriate.\n",
+    "df['average_montly_hours'].plot(kind='hist');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['average_montly_hours_100'] = df['average_montly_hours']/100.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['average_montly_hours_100'].plot(kind='hist');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['time_spend_company'].plot(kind='hist');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert the categorical features into binary dummy columns.\n",
+    "# You will then have to combine them with\n",
+    "# the numerical features using `pd.concat`.\n",
+    "df_dummies = pd.get_dummies(df[['sales', 'salary']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dummies.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.concat([df[['satisfaction_level', 'last_evaluation', 'number_project',\n",
+    "                   'time_spend_company', 'Work_accident',\n",
+    "                   'promotion_last_5years', 'average_montly_hours_100']],\n",
+    "               df_dummies], axis=1).values\n",
+    "y = df['left'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# do the usual train/test split with a 20% test size\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# play around with learning rate and optimizer\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_dim=20, activation='sigmoid'))\n",
+    "model.compile(Adam(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_pred = model.predict_classes(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pretty_confusion_matrix(y_true, y_pred, labels=[\"False\", \"True\"]):\n",
+    "    cm = confusion_matrix(y_true, y_pred)\n",
+    "    pred_labels = ['Predicted '+ l for l in labels]\n",
+    "    df = pd.DataFrame(cm, index=labels, columns=pred_labels)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the confusion matrix, precision and recall\n",
+    "\n",
+    "pretty_confusion_matrix(y_test, y_test_pred, labels=['Stay', 'Leave'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(y_test, y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check if you still get the same results if you use a 5-Fold cross validation on all the data\n",
+    "\n",
+    "def build_logistic_regression_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(1, input_dim=20, activation='sigmoid'))\n",
+    "    model.compile(Adam(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "model = KerasClassifier(build_fn=build_logistic_regression_model,\n",
+    "                        epochs=10, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold, cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = KFold(5, shuffle=True)\n",
+    "scores = cross_val_score(model, X, y, cv=cv)\n",
+    "\n",
+    "print(\"The cross validation accuracy is {:0.4f} ± {:0.4f}\".format(scores.mean(), scores.std()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Is the model good enough for your boss?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No, the model is not good enough for my boss, since it performs no better than the benchmark."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/solutions/4 Deep Learning Intro Exercises Solution.ipynb b/solutions/4 Deep Learning Intro Exercises Solution.ipynb
new file mode 100644
index 0000000..712f75a
--- /dev/null
+++ b/solutions/4 Deep Learning Intro Exercises Solution.ipynb	
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Learning Intro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The [Pima Indians dataset](https://archive.ics.uci.edu/ml/datasets/diabetes) is a very famous dataset distributed by UCI and originally collected from the National Institute of Diabetes and Digestive and Kidney Diseases. It contains data from clinical exams for women age 21 and above of Pima indian origins. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n",
+    "\n",
+    "It has the following features:\n",
+    "\n",
+    "- Pregnancies: Number of times pregnant\n",
+    "- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
+    "- BloodPressure: Diastolic blood pressure (mm Hg)\n",
+    "- SkinThickness: Triceps skin fold thickness (mm)\n",
+    "- Insulin: 2-Hour serum insulin (mu U/ml)\n",
+    "- BMI: Body mass index (weight in kg/(height in m)^2)\n",
+    "- DiabetesPedigreeFunction: Diabetes pedigree function\n",
+    "- Age: Age (years)\n",
+    "\n",
+    "The last colum is the outcome, and it is a binary variable.\n",
+    "\n",
+    "In this first exercise we will explore it through the following steps:\n",
+    "\n",
+    "1. Load the ..data/diabetes.csv dataset, use pandas to explore the range of each feature\n",
+    "- For each feature draw a histogram. Bonus points if you draw all the histograms in the same figure.\n",
+    "- Explore correlations of features with the outcome column. You can do this in several ways, for example using the `sns.pairplot` we used above or drawing a heatmap of the correlations.\n",
+    "- Do features need standardization? If so what stardardization technique will you use? MinMax? Standard?\n",
+    "- Prepare your final `X` and `y` variables to be used by a ML model. Make sure you define your target variable well. Will you need dummy columns?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/diabetes.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = df.hist(figsize=(12, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(df, hue='Outcome');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.heatmap(df.corr(), annot = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler()\n",
+    "X = sc.fit_transform(df.drop('Outcome', axis=1))\n",
+    "y = df['Outcome'].values\n",
+    "y_cat = to_categorical(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Build a fully connected NN model that predicts diabetes. Follow these steps:\n",
+    "\n",
+    "1. Split your data in a train/test with a test size of 20% and a `random_state = 22`\n",
+    "- define a sequential model with at least one inner layer. You will have to make choices for the following things:\n",
+    "    - what is the size of the input?\n",
+    "    - how many nodes will you use in each layer?\n",
+    "    - what is the size of the output?\n",
+    "    - what activation functions will you use in the inner layers?\n",
+    "    - what activation function will you use at output?\n",
+    "    - what loss function will you use?\n",
+    "    - what optimizer will you use?\n",
+    "- fit your model on the training set, using a validation_split of 0.1\n",
+    "- test your trained model on the test data from the train/test split\n",
+    "- check the accuracy score, the confusion matrix and the classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_cat,\n",
+    "                                                    random_state=22,\n",
+    "                                                    test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(32, input_shape=(8,), activation='relu'))\n",
+    "model.add(Dense(32, activation='relu'))\n",
+    "model.add(Dense(2, activation='softmax'))\n",
+    "model.compile(Adam(learning_rate=0.05),\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "32*8 + 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train, epochs=20, verbose=2, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_class = np.argmax(y_test, axis=1)\n",
+    "y_pred_class = np.argmax(y_pred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.Series(y_test_class).value_counts() / len(y_test_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_score(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(y_test_class, y_pred_class))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "confusion_matrix(y_test_class, y_pred_class)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "Compare your work with the results presented in [this notebook](https://www.kaggle.com/sheshu/pima-data-visualisation-and-machine-learning). Are your Neural Network results better or worse than the results obtained by traditional Machine Learning techniques?\n",
+    "\n",
+    "- Try training a Support Vector Machine or a Random Forest model on the exact same train/test split. Is the performance better or worse?\n",
+    "- Try restricting your features to only 4 features like in the suggested notebook. How does model performance change?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "for mod in [RandomForestClassifier(), SVC(), GaussianNB()]:\n",
+    "    mod.fit(X_train, y_train[:, 1])\n",
+    "    y_pred = mod.predict(X_test)\n",
+    "    print(\"=\"*80)\n",
+    "    print(mod)\n",
+    "    print(\"-\"*80)\n",
+    "    print(\"Accuracy score: {:0.3}\".format(accuracy_score(y_test_class,\n",
+    "                                                         y_pred)))\n",
+    "    print(\"Confusion Matrix:\")\n",
+    "    print(confusion_matrix(y_test_class, y_pred))\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4\n",
+    "\n",
+    "[Tensorflow playground](http://playground.tensorflow.org/) is a web based neural network demo. It is really useful to develop an intuition about what happens when you change architecture, activation function or other parameters. Try playing with it for a few minutes. You don't nee do understand the meaning of every knob and button in the page, just get a sense for what happens if you change something. In the next chapter we'll explore these things in more detail.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/solutions/5 Gradient Descent Exercises Solution.ipynb b/solutions/5 Gradient Descent Exercises Solution.ipynb
new file mode 100644
index 0000000..c0930b6
--- /dev/null
+++ b/solutions/5 Gradient Descent Exercises Solution.ipynb	
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 1\n",
+    "\n",
+    "You've just been hired at a wine company and they would like you to help them build a model that predicts the quality of their wine based on several measurements. They give you a dataset with wine\n",
+    "\n",
+    "- Load the ../data/wines.csv into Pandas\n",
+    "- Use the column called \"Class\" as target\n",
+    "- Check how many classes are there in target, and if necessary use dummy columns for a multi-class classification\n",
+    "- Use all the other columns as features, check their range and distribution (using seaborn pairplot)\n",
+    "- Rescale all the features using either MinMaxScaler or StandardScaler\n",
+    "- Build a deep model with at least 1 hidden layer to classify the data\n",
+    "- Choose the cost function, what will you use? Mean Squared Error? Binary Cross-Entropy? Categorical Cross-Entropy?\n",
+    "- Choose an optimizer\n",
+    "- Choose a value for the learning rate, you may want to try with several values\n",
+    "- Choose a batch size\n",
+    "- Train your model on all the data using a `validation_split=0.2`. Can you converge to 100% validation accuracy?\n",
+    "- What's the minumum number of epochs to converge?\n",
+    "- Repeat the training several times to verify how stable your results are"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/wines.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df['Class']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat = pd.get_dummies(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_cat.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop('Class', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(df, hue='Class')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Xsc = sc.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import SGD, Adam, Adadelta, RMSprop\n",
+    "import tensorflow.keras.backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "model.add(Dense(5, input_shape=(13,),\n",
+    "                kernel_initializer='he_normal',\n",
+    "                activation='relu'))\n",
+    "model.add(Dense(3, activation='softmax'))\n",
+    "\n",
+    "model.compile(RMSprop(learning_rate=0.1),\n",
+    "              'categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(Xsc, y_cat.values,\n",
+    "          batch_size=8,\n",
+    "          epochs=10,\n",
+    "          verbose=1,\n",
+    "          validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 2\n",
+    "\n",
+    "Since this dataset has 13 features we can only visualize pairs of features like we did in the Paired plot. We could however exploit the fact that a neural network is a function to extract 2 high level features to represent our data.\n",
+    "\n",
+    "- Build a deep fully connected network with the following structure:\n",
+    "    - Layer 1: 8 nodes\n",
+    "    - Layer 2: 5 nodes\n",
+    "    - Layer 3: 2 nodes\n",
+    "    - Output : 3 nodes\n",
+    "- Choose activation functions, inizializations, optimizer and learning rate so that it converges to 100% accuracy within 20 epochs (not easy)\n",
+    "- Remember to train the model on the scaled data\n",
+    "- Define a Feature Function like we did above between the input of the 1st layer and the output of the 3rd layer\n",
+    "- Calculate the features and plot them on a 2-dimensional scatter plot\n",
+    "- Can we distinguish the 3 classes well?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "model.add(Dense(8, input_shape=(13,),\n",
+    "                kernel_initializer='he_normal', activation='tanh'))\n",
+    "model.add(Dense(5, kernel_initializer='he_normal', activation='tanh'))\n",
+    "model.add(Dense(2, kernel_initializer='he_normal', activation='tanh'))\n",
+    "model.add(Dense(3, activation='softmax'))\n",
+    "\n",
+    "model.compile(RMSprop(learning_rate=0.05),\n",
+    "              'categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(Xsc, y_cat.values,\n",
+    "          batch_size=16,\n",
+    "          epochs=20,\n",
+    "          verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inp = model.layers[0].input\n",
+    "out = model.layers[2].output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_function = K.function([inp], [out])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = features_function([Xsc])[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(features[:, 0], features[:, 1], c=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 3\n",
+    "\n",
+    "Keras functional API. So far we've always used the Sequential model API in Keras. However, Keras also offers a Functional API, which is much more powerful. You can find its [documentation here](https://keras.io/getting-started/functional-api-guide/). Let's see how we can leverage it.\n",
+    "\n",
+    "- define an input layer called `inputs`\n",
+    "- define two hidden layers as before, one with 8 nodes, one with 5 nodes\n",
+    "- define a `second_to_last` layer with 2 nodes\n",
+    "- define an output layer with 3 nodes\n",
+    "- create a model that connect input and output\n",
+    "- train it and make sure that it converges\n",
+    "- define a function between inputs and second_to_last layer\n",
+    "- recalculate the features and plot them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Input\n",
+    "from tensorflow.keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "inputs = Input(shape=(13,))\n",
+    "x = Dense(8, kernel_initializer='he_normal', activation='tanh')(inputs)\n",
+    "x = Dense(5, kernel_initializer='he_normal', activation='tanh')(x)\n",
+    "second_to_last = Dense(2, kernel_initializer='he_normal',\n",
+    "                       activation='tanh')(x)\n",
+    "outputs = Dense(3, activation='softmax')(second_to_last)\n",
+    "\n",
+    "model = Model(inputs=inputs, outputs=outputs)\n",
+    "\n",
+    "model.compile(RMSprop(learning_rate=0.05),\n",
+    "              'categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(Xsc, y_cat.values, batch_size=16, epochs=20, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_function = K.function([inputs], [second_to_last])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = features_function([Xsc])[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(features[:, 0], features[:, 1], c=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4 \n",
+    "\n",
+    "Keras offers the possibility to call a function at each epoch. These are Callbacks, and their [documentation is here](https://keras.io/callbacks/). Callbacks allow us to add some neat functionality. In this exercise we'll explore a few of them.\n",
+    "\n",
+    "- Split the data into train and test sets with a test_size = 0.3 and random_state=42\n",
+    "- Reset and recompile your model\n",
+    "- train the model on the train data using `validation_data=(X_test, y_test)`\n",
+    "- Use the `EarlyStopping` callback to stop your training if the `val_loss` doesn't improve\n",
+    "- Use the `ModelCheckpoint` callback to save the trained model to disk once training is finished\n",
+    "- Use the `TensorBoard` callback to output your training information to a `/tmp/` subdirectory\n",
+    "- Watch the next video for an overview of tensorboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpointer = ModelCheckpoint(filepath=\"/tmp/udemy/weights.hdf5\",\n",
+    "                               verbose=1, save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earlystopper = EarlyStopping(monitor='val_loss', min_delta=0,\n",
+    "                             patience=1, verbose=1, mode='auto')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tensorboard = TensorBoard(log_dir='/tmp/udemy/tensorboard/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(Xsc, y_cat.values,\n",
+    "                                                    test_size=0.3,\n",
+    "                                                    random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "inputs = Input(shape=(13,))\n",
+    "\n",
+    "x = Dense(8, kernel_initializer='he_normal', activation='tanh')(inputs)\n",
+    "x = Dense(5, kernel_initializer='he_normal', activation='tanh')(x)\n",
+    "second_to_last = Dense(2, kernel_initializer='he_normal',\n",
+    "                       activation='tanh')(x)\n",
+    "outputs = Dense(3, activation='softmax')(second_to_last)\n",
+    "\n",
+    "model = Model(inputs=inputs, outputs=outputs)\n",
+    "\n",
+    "model.compile(RMSprop(learning_rate=0.05), 'categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, y_train, batch_size=32,\n",
+    "          epochs=20, verbose=2,\n",
+    "          validation_data=(X_test, y_test),\n",
+    "          callbacks=[checkpointer, earlystopper, tensorboard])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run Tensorboard with the command:\n",
+    "\n",
+    "    tensorboard --logdir /tmp/udemy/tensorboard/\n",
+    "    \n",
+    "and open your browser at http://localhost:6006"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/solutions/6 Convolutional Neural Networks Exercises Solution.ipynb b/solutions/6 Convolutional Neural Networks Exercises Solution.ipynb
new file mode 100644
index 0000000..15f5bba
--- /dev/null
+++ b/solutions/6 Convolutional Neural Networks Exercises Solution.ipynb	
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Neural Networks Exercises Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
+    "import tensorflow.keras.backend as K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Exercise 1\n",
+    "\n",
+    "You've been hired by a shipping company to overhaul the way they route mail, parcels and packages. They want to build an image recognition system  capable of recognizing the digits in the zipcode on a package, so that it can be automatically routed to the correct location.\n",
+    "You are tasked to build the digit recognition system. Luckily, you can rely on the MNIST dataset for the intial training of your model!\n",
+    "\n",
+    "Build a deep convolutional neural network with at least two convolutional and two pooling layers before the fully connected layer.\n",
+    "\n",
+    "- Start from the network we have just built\n",
+    "- Insert a `Conv2D` layer after the first `MaxPool2D`, give it 64 filters.\n",
+    "- Insert a `MaxPool2D` after that one\n",
+    "- Insert an `Activation` layer\n",
+    "- retrain the model\n",
+    "- does performance improve?\n",
+    "- how many parameters does this new model have? More or less than the previous model? Why?\n",
+    "- how long did this second model take to train? Longer or shorter than the previous model? Why?\n",
+    "- did it perform better or worse than the previous model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data(('/tmp/mnist.npz'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32') / 255.0\n",
+    "X_test = X_test.astype('float32') / 255.0\n",
+    "\n",
+    "X_train = X_train.reshape(-1, 28, 28, 1)\n",
+    "X_test = X_test.reshape(-1, 28, 28, 1)\n",
+    "\n",
+    "y_train_cat = to_categorical(y_train, 10)\n",
+    "y_test_cat = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
+    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train_cat, batch_size=128,\n",
+    "          epochs=2, verbose=1, validation_split=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.evaluate(X_test, y_test_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 2\n",
+    "\n",
+    "Pleased with your performance with the digits recognition task, your boss decides to challenge you with a harder task. Their online branch allows people to upload images to a website that generates and prints a postcard that is shipped to destination. Your boss would like to know what images people are loading on the site in order to provide targeted advertising on the same page, so he asks you to build an image recognition system capable of recognizing a few objects. Luckily for you, there's a dataset ready made with a collection of labeled images. This is the [Cifar 10 Dataset](http://www.cs.toronto.edu/~kriz/cifar.html), a very famous dataset that contains images for 10 different categories:\n",
+    "\n",
+    "- airplane \t\t\t\t\t\t\t\t\t\t\n",
+    "- automobile \t\t\t\t\t\t\t\t\t\t\n",
+    "- bird \t\t\t\t\t\t\t\t\t\t\n",
+    "- cat \t\t\t\t\t\t\t\t\t\t\n",
+    "- deer \t\t\t\t\t\t\t\t\t\t\n",
+    "- dog \t\t\t\t\t\t\t\t\t\t\n",
+    "- frog \t\t\t\t\t\t\t\t\t\t\n",
+    "- horse \t\t\t\t\t\t\t\t\t\t\n",
+    "- ship \t\t\t\t\t\t\t\t\t\t\n",
+    "- truck\n",
+    "\n",
+    "In this exercise we will reach the limit of what you can achieve on your laptop and get ready for the next session on cloud GPUs.\n",
+    "\n",
+    "Here's what you have to do:\n",
+    "- load the cifar10 dataset using `keras.datasets.cifar10.load_data()`\n",
+    "- display a few images, see how hard/easy it is for you to recognize an object with such low resolution\n",
+    "- check the shape of X_train, does it need reshape?\n",
+    "- check the scale of X_train, does it need rescaling?\n",
+    "- check the shape of y_train, does it need reshape?\n",
+    "- build a model with the following architecture, and choose the parameters and activation functions for each of the layers:\n",
+    "    - conv2d\n",
+    "    - conv2d\n",
+    "    - maxpool\n",
+    "    - conv2d\n",
+    "    - conv2d\n",
+    "    - maxpool\n",
+    "    - flatten\n",
+    "    - dense\n",
+    "    - output\n",
+    "- compile the model and check the number of parameters\n",
+    "- attempt to train the model with the optimizer of your choice. How fast does training proceed?\n",
+    "- If training is too slow (as expected) stop the execution and move to the next session!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import cifar10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(X_train[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32') / 255.0\n",
+    "X_test = X_test.astype('float32') / 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_cat = to_categorical(y_train, 10)\n",
+    "y_test_cat = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_cat.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, (3, 3),\n",
+    "                 padding='same',\n",
+    "                 input_shape=(32, 32, 3),\n",
+    "                 activation='relu'))\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
+    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(512, activation='relu'))\n",
+    "model.add(Dense(10, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train_cat,\n",
+    "          batch_size=32,\n",
+    "          epochs=2,\n",
+    "          validation_data=(X_test, y_test_cat),\n",
+    "          shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb b/solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb
new file mode 100644
index 0000000..76f49e1
--- /dev/null
+++ b/solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb	
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recurrent Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time series forecasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.offsets import MonthEnd\n",
+    "\n",
+    "df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',\n",
+    "                 skiprows=6, skipfooter=9,\n",
+    "                 engine='python')\n",
+    "\n",
+    "df['Adjustments'] = pd.to_datetime(df['Adjustments']) + MonthEnd(1)\n",
+    "df = df.set_index('Adjustments')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_date = pd.Timestamp('01-01-2011')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = df.loc[:split_date, ['Unadjusted']]\n",
+    "test = df.loc[split_date:, ['Unadjusted']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "sc = MinMaxScaler()\n",
+    "\n",
+    "train_sc = sc.fit_transform(train)\n",
+    "test_sc = sc.transform(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_sc_df = pd.DataFrame(train_sc, columns=['Scaled'], index=train.index)\n",
+    "test_sc_df = pd.DataFrame(test_sc, columns=['Scaled'], index=test.index)\n",
+    "\n",
+    "for s in range(1, 13):\n",
+    "    train_sc_df['shift_{}'.format(s)] = train_sc_df['Scaled'].shift(s)\n",
+    "    test_sc_df['shift_{}'.format(s)] = test_sc_df['Scaled'].shift(s)\n",
+    "\n",
+    "X_train = train_sc_df.dropna().drop('Scaled', axis=1)\n",
+    "y_train = train_sc_df.dropna()[['Scaled']]\n",
+    "\n",
+    "X_test = test_sc_df.dropna().drop('Scaled', axis=1)\n",
+    "y_test = test_sc_df.dropna()[['Scaled']]\n",
+    "\n",
+    "X_train = X_train.values\n",
+    "X_test= X_test.values\n",
+    "\n",
+    "y_train = y_train.values\n",
+    "y_test = y_test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "In the model above we reshaped the input shape to: `(num_samples, 1, 12)`, i.e. we treated a window of 12 months as a vector of 12 coordinates that we simultaneously passed to all the LSTM nodes. An alternative way to look at the problem is to reshape the input to `(num_samples, 12, 1)`. This means we consider each input window as a sequence of 12 values that we will pass in sequence to the LSTM. In principle this looks like a more accurate description of our situation. But does it yield better predictions? Let's check it.\n",
+    "\n",
+    "- Reshape `X_train` and `X_test` so that they represent a set of univariate sequences\n",
+    "- retrain the same LSTM(6) model, you'll have to adapt the `input_shape`\n",
+    "- check the performance of this new model, is it better at predicting the test data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_t = X_train.reshape(X_train.shape[0], 12, 1)\n",
+    "X_test_t = X_test.reshape(X_test.shape[0], 12, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import LSTM, Dense\n",
+    "import tensorflow.keras.backend as K\n",
+    "from tensorflow.keras.callbacks import EarlyStopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(LSTM(6, input_shape=(12, 1)))\n",
+    "\n",
+    "model.add(Dense(1))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train_t, y_train, epochs=600,\n",
+    "          batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test_t)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "RNN models can be applied to images too. In general we can apply them to any data where there's a connnection between nearby units. Let's see how we can easily build a model that works with images.\n",
+    "\n",
+    "- Load the MNIST data, by now you should be able to do it blindfolded :)\n",
+    "- reshape it so that an image looks like a long sequence of pixels\n",
+    "- create a recurrent model and train it on the training data\n",
+    "- how does it perform compared to a fully connected? How does it compare to Convolutional Neural Networks?\n",
+    "\n",
+    "(feel free to run this exercise on a cloud GPU if it's too slow on your laptop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import mnist\n",
+    "from tensorflow.keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "X_train = X_train.astype('float32') / 255.0\n",
+    "X_test = X_test.astype('float32') / 255.0\n",
+    "y_train_cat = to_categorical(y_train, 10)\n",
+    "y_test_cat = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.reshape(X_train.shape[0], -1, 1)\n",
+    "X_test = X_test.reshape(X_test.shape[0], -1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(X_train.shape)\n",
+    "print(X_test.shape)\n",
+    "print(y_train_cat.shape)\n",
+    "print(y_test_cat.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the model\n",
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(32, input_shape=X_train.shape[1:]))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "# compile the model\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, y_train_cat,\n",
+    "          batch_size=32,\n",
+    "          epochs=100,\n",
+    "          validation_split=0.3,\n",
+    "          shuffle=True,\n",
+    "          verbose=2,\n",
+    "          )\n",
+    "\n",
+    "model.evaluate(X_test, y_test_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/solutions/9 Improving performance Exercises Solutions.ipynb b/solutions/9 Improving performance Exercises Solutions.ipynb
new file mode 100644
index 0000000..97ba008
--- /dev/null
+++ b/solutions/9 Improving performance Exercises Solutions.ipynb	
@@ -0,0 +1,520 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 9 Improving performance Exercises Solutions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1\n",
+    "\n",
+    "- Reload the IMDB data keeping only the first 20000 most common words\n",
+    "- pad the reviews to a shorter length (eg. 70 or 80), this time make sure you keep the first part of the review if it's longer than the maximum length\n",
+    "- re run the model (remember to set max_features correctly)\n",
+    "- does it train faster this time?\n",
+    "- do you get a better performance?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import imdb\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Embedding, LSTM, Dense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_features = 20000\n",
+    "skip_top = 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = imdb.load_data('/tmp/imdb.npz',\n",
+    "                                                      num_words=max_features,\n",
+    "                                                      start_char=1,\n",
+    "                                                      oov_char=2,\n",
+    "                                                      index_from=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maxlen = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_pad = pad_sequences(X_train, maxlen=maxlen, truncating='post')\n",
+    "X_test_pad = pad_sequences(X_test, maxlen=maxlen, truncating='post')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Embedding(max_features, 128))\n",
+    "model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer='adam',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train_pad, y_train,\n",
+    "          batch_size=32,\n",
+    "          epochs=2,\n",
+    "          validation_split=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score, acc = model.evaluate(X_test_pad, y_test)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2\n",
+    "\n",
+    "- Reload the digits data as above\n",
+    "- define a function repeated_training_reg_dropout that adds regularization and dropout to a fully connected network\n",
+    "- compare the performance with/witouth dropout and regularization like we did for batch normalization\n",
+    "- do you get a better performance?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from tensorflow.keras.layers import Dropout\n",
+    "import tensorflow.keras.backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = load_digits()\n",
+    "X, y = digits.data, digits.target\n",
+    "y_cat = to_categorical(y)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_cat, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def repeated_training_reg_dropout(X_train,\n",
+    "                                  y_train,\n",
+    "                                  X_test,\n",
+    "                                  y_test,\n",
+    "                                  units=512,\n",
+    "                                  activation='sigmoid',\n",
+    "                                  optimizer='sgd',\n",
+    "                                  do_dropout=False,\n",
+    "                                  rate=0.3,\n",
+    "                                  kernel_regularizer='l2',\n",
+    "                                  epochs=10,\n",
+    "                                  repeats=3):\n",
+    "    histories = []\n",
+    "    \n",
+    "    for repeat in range(repeats):\n",
+    "        K.clear_session()\n",
+    "\n",
+    "        model = Sequential()\n",
+    "        \n",
+    "        # first fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        input_shape=X_train.shape[1:],\n",
+    "                        kernel_initializer='normal',\n",
+    "                        kernel_regularizer=kernel_regularizer,\n",
+    "                        activation=activation))\n",
+    "        if do_dropout:\n",
+    "            model.add(Dropout(rate))\n",
+    "\n",
+    "        # second fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        kernel_initializer='normal',\n",
+    "                        kernel_regularizer=kernel_regularizer,\n",
+    "                        activation=activation))\n",
+    "        if do_dropout:\n",
+    "            model.add(Dropout(rate))\n",
+    "\n",
+    "        # third fully connected layer\n",
+    "        model.add(Dense(units,\n",
+    "                        kernel_initializer='normal',\n",
+    "                        kernel_regularizer=kernel_regularizer,\n",
+    "                        activation=activation))\n",
+    "        if do_dropout:\n",
+    "            model.add(Dropout(rate))\n",
+    "\n",
+    "        # output layer\n",
+    "        model.add(Dense(10, activation='softmax'))\n",
+    "        \n",
+    "        model.compile(optimizer,\n",
+    "                      'categorical_crossentropy',\n",
+    "                      metrics=['accuracy'])\n",
+    "\n",
+    "        h = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, verbose=0)\n",
+    "        histories.append([h.history['accuracy'], h.history['val_accuracy']])\n",
+    "        print(repeat, end=' ')\n",
+    "\n",
+    "    histories = np.array(histories)\n",
+    "    \n",
+    "    # calculate mean and standard deviation across repeats:\n",
+    "    mean_acc = histories.mean(axis=0)\n",
+    "    std_acc = histories.std(axis=0)\n",
+    "    print()\n",
+    "    \n",
+    "    return mean_acc[0], std_acc[0], mean_acc[1], std_acc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_acc, std_acc, mean_acc_val, std_acc_val = repeated_training_reg_dropout(X_train,\n",
+    "                                                                             y_train,\n",
+    "                                                                             X_test,\n",
+    "                                                                             y_test,\n",
+    "                                                                             do_dropout=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_acc_do, std_acc_do, mean_acc_val_do, std_acc_val_do = repeated_training_reg_dropout(X_train,\n",
+    "                                                                                         y_train,\n",
+    "                                                                                         X_test,\n",
+    "                                                                                         y_test,\n",
+    "                                                                                         do_dropout=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_mean_std(m, s):\n",
+    "    plt.plot(m)\n",
+    "    plt.fill_between(range(len(m)), m-s, m+s, alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_mean_std(mean_acc, std_acc)\n",
+    "plot_mean_std(mean_acc_val, std_acc_val)\n",
+    "plot_mean_std(mean_acc_do, std_acc_do)\n",
+    "plot_mean_std(mean_acc_val_do, std_acc_val_do)\n",
+    "plt.ylim(0, 1.01)\n",
+    "plt.title(\"Dropout and Regularization Accuracy\")\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend(['Train', 'Test', 'Train with Dropout and Regularization', 'Test with Dropout and Regularization'], loc='best')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3\n",
+    "\n",
+    "This is a very long and complex exercise, that should give you an idea of a real world scenario. Feel free to look at the solution if you feel lost. Also, feel free to run this with a GPU, in which case you don't need to download the data.\n",
+    "\n",
+    "If you are running this locally, download and unpack the male/female pictures from [here](https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=0). These images and labels were obtained from [Crowdflower](https://www.crowdflower.com/data-for-everyone/).\n",
+    "\n",
+    "Your goal is to build an image classifier that will recognize the gender of a person from pictures.\n",
+    "\n",
+    "- Have a look at the directory structure and inspect a couple of pictures\n",
+    "- Design a model that will take a color image of size 64x64 as input and return a binary output (female=0/male=1)\n",
+    "- Feel free to introduce any regularization technique in your model (Dropout, Batch Normalization, Weight Regularization)\n",
+    "- Compile your model with an optimizer of your choice\n",
+    "- Using `ImageDataGenerator`, define a train generator that will augment your images with some geometric transformations. Feel free to choose the parameters that make sense to you.\n",
+    "- Define also a test generator, whose only purpose is to rescale the pixels by 1./255\n",
+    "- use the function `flow_from_directory` to generate batches from the train and test folders. Make sure you set the `target_size` to 64x64.\n",
+    "- Use the `model.fit_generator` function to fit the model on the batches generated from the ImageDataGenerator. Since you are streaming and augmenting the data in real time you will have to decide how many batches make an epoch and how many epochs you want to run\n",
+    "- Train your model (you should get to at least 85% accuracy)\n",
+    "- Once you are satisfied with your training, check a few of the misclassified pictures. Are those sensible errors?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you are running this locally\n",
+    "# uncomment the next 4 lines to download, extract and set the data path:\n",
+    "# !wget 'https://www.dropbox.com/s/nov493om2jmh2gp/male_female.tgz?dl=1' -O ../data/male_female.tgz\n",
+    "# data_path = '../data/male_female'\n",
+    "# !mkdir -p {data_path}\n",
+    "# !tar -xzvf ../data/male_female.tgz --directory {data_path}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Conv2D\n",
+    "from tensorflow.keras.layers import MaxPooling2D\n",
+    "from tensorflow.keras.layers import Flatten\n",
+    "from tensorflow.keras.layers import BatchNormalization\n",
+    "from itertools import islice\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K.clear_session()\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n",
+    "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
+    "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
+    "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "model.add(Dense(128, activation = 'relu'))\n",
+    "model.add(Dense(1, activation = 'sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer = 'adam',\n",
+    "              loss = 'binary_crossentropy',\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_gen = ImageDataGenerator(rescale = 1./255,\n",
+    "                               width_shift_range=0.1,\n",
+    "                               height_shift_range=0.1,\n",
+    "                               rotation_range = 10,\n",
+    "                               shear_range = 0.2,\n",
+    "                               zoom_range = 0.2,\n",
+    "                               horizontal_flip = True)\n",
+    "\n",
+    "test_gen = ImageDataGenerator(rescale = 1./255)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = train_gen.flow_from_directory(data_path + '/train',\n",
+    "                                      target_size = (64, 64),\n",
+    "                                      batch_size = 16,\n",
+    "                                      class_mode = 'binary')\n",
+    "\n",
+    "test = test_gen.flow_from_directory(data_path + '/test',\n",
+    "                                    target_size = (64, 64),\n",
+    "                                    batch_size = 16,\n",
+    "                                    class_mode = 'binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(train,\n",
+    "          steps_per_epoch = 800,\n",
+    "          epochs = 200,\n",
+    "          validation_data = test,\n",
+    "          validation_steps = 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = []\n",
+    "y_test = []\n",
+    "for ts in islice(test, 50):\n",
+    "    X_test.append(ts[0])\n",
+    "    y_test.append(ts[1])\n",
+    "\n",
+    "X_test = np.concatenate(X_test)\n",
+    "y_test = np.concatenate(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict_classes(X_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.argwhere(y_test != y_pred).ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(X_test[14])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/test_nb.py b/tests/test_nb.py
new file mode 100644
index 0000000..700de90
--- /dev/null
+++ b/tests/test_nb.py
@@ -0,0 +1,77 @@
+# tests that too long to execute on Travis are temporarily commented out
+# TODO: find a way to fix this
+
+import subprocess
+import tempfile
+
+
+def _exec_notebook(path):
+    with tempfile.NamedTemporaryFile(suffix=".ipynb") as fout:
+        args = ["jupyter", "nbconvert", "--to", "notebook", "--execute",
+                "--ExecutePreprocessor.timeout=1000",
+                "--output", fout.name, path]
+        subprocess.check_call(args)
+
+
+def test_0():
+    _exec_notebook('course/0_Check_Environment.ipynb')
+
+
+def test_1():
+    _exec_notebook('course/1 First Deep Learning Model.ipynb')
+
+
+def test_2():
+    _exec_notebook('course/2 Data.ipynb')
+
+
+def test_3():
+    _exec_notebook('course/3 Machine Learning.ipynb')
+
+
+def test_4():
+    _exec_notebook('course/4 Deep Learning Intro.ipynb')
+
+
+def test_5():
+    _exec_notebook('course/5 Gradient Descent.ipynb')
+
+
+def test_6():
+    _exec_notebook('course/6 Convolutional Neural Networks.ipynb')
+
+
+def test_8():
+    _exec_notebook('course/8 Recurrent Neural Networks.ipynb')
+
+
+def test_9():
+    _exec_notebook('course/9 Improving performance.ipynb')
+
+
+def test_2_sol():
+    _exec_notebook('solutions/2 Data exploration Exercises Solution.ipynb')
+
+
+def test_3_sol():
+    _exec_notebook('solutions/3 Machine Learning Exercises Solution.ipynb')
+
+
+def test_4_sol():
+    _exec_notebook('solutions/4 Deep Learning Intro Exercises Solution.ipynb')
+
+
+def test_5_sol():
+    _exec_notebook('solutions/5 Gradient Descent Exercises Solution.ipynb')
+
+
+def test_6_sol():
+    _exec_notebook('solutions/6 Convolutional Neural Networks Exercises Solution.ipynb')
+
+
+def test_8_sol():
+    _exec_notebook('solutions/8 Recurrent Neural Networks Exercises Solutions.ipynb')
+
+
+def test_9_sol():
+    _exec_notebook('solutions/9 Improving performance Exercises Solutions.ipynb')