Glad to see JAX featured alongside PyTorch. JAX still feels like the best-kept secret in deep learning

Damn beeeeefffffyyyyy. Need the month to eat ten pages a day, Tnx looks awesome. Could append diffusion too ultimately

Wow, kudos to the Author. Very easy to digest, beautifully crafted, and took the time to explain the concepts when most places take them for granted.

This looks like a good practical companion for a more theoretical text, such as Deep Learning by Bishop.

Although I love this, it's not peer reviewed and I don't trust arxiv.

Website of the author with more material and lab sessions

https://www.sscardapane.it/alice-book/

https://sscardapane.notion.site/Guided-lab-sessions-18c25bd1...

It would be nice if arXiv included a small-layout pdf or native epub option for e-readers. Now that they serve the Tex files and are experimenting with HTML, it feels like a natural step.

  The corresponding row vector is denoted by x^T when we need to distinguish them. We can also ignore the transpose for readability, if the shape is clear from context.

I am tilting at windmills, but I am continually annoyed at the sloppiness of mathematicians in writing. Fine, you don’t like verbosity, but for didactic purposes, please do not assume the reader is equipped to know that variable x actually implies variable y.

All that being said, the writing style from the first chapter is very encouraging at how approachable this will be.

this 3 page classic [1] captures most of the core ideas and explains it in a manner anyone with basic calculus background can understand - "Learning representations by back-propagating errors"

[1] https://gwern.net/doc/ai/nn/1986-rumelhart-2.pdf