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. 😅

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Basic notebooks

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

PyTorch

JAX

Explainability