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README.md

Deep Learning course: lecture slides and lab notebooks

This is a short adaptation of the original course in Master Datascience Paris Saclay by Olivier Grisel and Charles Ollion

Table of contents

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

Lecture slides

  • Intro to Deep Learning (HTML | PDF)
  • Neural Networks and Backpropagation (HTML | PDF)
  • Convolutional Neural Networks for Image Classification (HTML | PDF)
  • Embeddings and Recommender systems (HTML | PDF)

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 https://github.com/rth/dl-lectures-labs

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

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

Lab 1: Neural Networks and Backpropagation

Lab 2: Convolutional Neural Networks for Image Classification

Lab 3: Embedding and Recommender systems

Lab 4: Natual Language Processing

Acknowledgments

The original 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.

License

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.

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Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay

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