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I think this one is a bit out of date....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