Chapter | Title | Summary | Notes | Videos | Exercises |
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1 | Deep learning vs. classical ML and optimization | html | 1, 2 | Hyperparameter and training basics | |
2 | Interpretability | html | 1, 2, 3 | Visualization with grad-CAM | |
3 | Architectures | html | 1 | Graph-NN: code and instructions | |
4 | Small data: weak supervision, transfer, and incorporation of priors | 1 + 2 | 1 | Transfer learning: instructions and jupyter notebook | |
* | Presentation of Therapixel, start-up in medical imaging, by Yaroslav Nikulin Evolutionary Deep Computer Vision, by Olivier Teytaud (Facebook FAIR Paris) | slides+video slides + videos: 1, 2 |
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5 | Deep learning for physics, with Lionel Mathelin (LIMSI, Paris-Sud) and Michele Alessandro Bucci (LRI, TAU team) | (html) | slides | 1, 2 | Learning dynamical systems, with presentation |
6 | Modeling tasks and losses + Generative models (GAN, VAE and Normalizing Flows) | html - | 1 2, TP+3 | Generative models |
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7 | Guarantees? Generalization and formal proofs + Auto-ML / Auto-DeepLearning, by Lisheng Sun (Isabelle Guyon's group, LRI, Paris-Sud) | (html) - | pdf slides | 1 2 |