https://www.deeplearningbook.org/ , PDF version: https://github.com/janishar/mit-deep-learning-book-pdf

Thank you so much!

I think this one is a bit out of date....

It looks like it covers the basics pretty well. Any pointers to alternatives?

The "Understanding Deep Learning" book covers more recent models as well: https://udlbook.github.io/udlbook/ (free PDF and Jupyter notebooks available)

IMVHO, GANs are entirely optional.

For others, https://web.stanford.edu/~jurafsky/slp3/ will take you a decent way to understanding transformer architecture.

For specifically understanding transformers, this (w/ maybe GPT-4 by your side to unpack jargon/math) might be able to get you from lay-person to understanding enough to be dangerous pretty quickly: https://sebastianraschka.com/blog/2023/llm-reading-list.html

An earlier edition of this book was the textbook for a computational linguistics course I took back in ~2002 (!), it's amazing how much has been added

The handouts and slides are there. They are fully self-contained, not mere bullet points that the lecturer then talks about.

That's fabulous! Thanks for pointing that out

This is my favourite text! It really drove home a lot points on many architectures for me. Their math appendix is simply amazing as well!

The deep learning book is a great choice, as many have mentioned.

I've been making a course that has a little less theory, and a little more application here - https://github.com/VikParuchuri/zero_to_gpt . Videos are all optional (cover the same content as the text).