How to demonstrate (or teach) learning theory using PyTorch

I know this is an old conversation but I am developing a full fledged package for information theory of deep learning in PyTorch which have a lot of information bottleneck functionalities including HSIC bottleneck sigma networks (yes ! that train without backprop). Currently library is in testing phase but there are notebooks available, thought that can be useful.
Link to the repo:


Link to the documentation:
https://pyglow.github.io/

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