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Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

Deep Learning course: lecture slides and lab notebooks

This course is being taught at as part of Master Year 2 Data Science IP-Paris

Table of contents

The course covers the basics of Deep Learning, with a focus on applications.

Lecture slides

Note: press “P” to display the presenter’s notes that include some comments and additional references.

Lab and Home Assignment Notebooks

The Jupyter notebooks for the labs can be found in the labs folder of the github repository:

git clone

These notebooks only work with keras and tensorflow Please follow the to get started.

Direct links to the rendered notebooks including solutions (to be updated in rendered mode):

Lab 1: Intro to Deep Learning

Lab 2: Neural Networks and Backpropagation

Lab 3: Embeddings and Recommender Systems

Lab 4: Convolutional Neural Networks for Image Classification

Lab 5: Deep Learning for Object Dection and Image Segmentation

Lab 6: Text Classification, Word Embeddings and Language Models

Lab 7: Sequence to Sequence for Machine Translation

Lab 8: Intro to PyTorch

Lab 9: Siamese Networks and Triplet loss

Lab 10: Variational Auto Encoder


This lecture is built and maintained by Olivier Grisel and Charles Ollion

Charles Ollion, head of research at Heuritech - Olivier Grisel, software engineer at Inria

We thank the Orange-Keyrus-Thalès chair for supporting this class.


All the code in this repository is made available under the MIT license unless otherwise noted.

The slides are published under the terms of the CC-By 4.0 license.