Chapter | Title | Summary | Notes | Videos | Exercises |
---|---|---|---|---|---|
1 | Deep learning vs. classical ML and optimization | html | 1 [2022] | Hyperparameter and training basics Environment and practical tips and tricks |
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2 | Interpretability | html | pdf 1, 2 | 1, 2, 3, 4 | Visualization with grad-CAM |
3 | Architectures | html | pdf 1, 2 | 1, 2, 3 | Graph-NN |
4 | Small data: weak supervision, transfer, and incorporation of priors | html | 1, 2 | Transfer learning: jupyter notebook | |
5 | Modeling: deep learning and physics (exploiting known invariances, priors or physical properties) | html | pdf 1, 2, + refs by A. Bucci | 1, 2, 3 | Learning dynamical systems (Credits: Alessandro Bucci) |
6 | Generative models (GAN, VAE, Normalizing Flows and Diffusion Models) | - | 1, 2 | Generative models |
|
7 | Modeling tasks and losses + Auto-ML / Auto-DeepLearning by Lisheng Sun-Hosoya and Romain Égelé from Isabelle Guyon's team | html | pdf, AutoML/MetaLearning slides, Neural Architecture Search slides | 1, 2 | - |