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This class is currently being given at Master Datascience Paris Saclay

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

• Neural Networks and Backpropagation (slides)
• Embeddings and Recommender Systems (slides)
• Convolutional Neural Networks for Image Classification (slides)
• Deep Learning for Object Dection and Image Segmentation (slides)
• Recurrent Neural Networks and NLP (slides)
• Expressivity, Optimization and Generalization

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

git clone https://github.com/m2dsupsdlclass/lectures-labs

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.