This is a short adaptation of the original course in Master Datascience Paris Saclay by Olivier Grisel and Charles Ollion
The course covers the basics of Deep Learning, with a focus on applications.
- Intro to Deep Learning
- Neural Networks and Backpropagation
- Convolutional Neural Networks for Image Classification
- Embeddings and Recommender systems
- Natural Language Processing
Note: press "P" to display the presenter's notes that include some comments and additional references.
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):
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.
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.