I am collecting here a series of (guided) exercises, implemented in Jupyter notebooks, to explore several topics in deep learning, from automatic differentiation to explainability. This is a work in progress that will take a lot of time, but most notebooks can already be used in a standalone form.

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Most exercises are relatively simple, so I suggest removing any code autocomplete features before starting. 😅 In Google Colab Settings >> AI Assistance >> Disable inline completions. You can easily re-turn them on for suggestions or solutions.

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Basic notebooks

All notebooks in this category are assumed essential and will not be listed below.

PyTorch

These are didactic notebooks for learning PyTorch.

JAX

These are roughly equivalent to the PyTorch ones, but in JAX (and corresponding high-level libraries, such as Equinox).

Automatic differentiation

Post-training

Any method that acts on one or more pre-trained models.

Explainability