There is a lot more. Just google for "deep learning", and you'll find a lot of content. And most of that will cover attention, as it is a really basic concept now.
To add to the excellent resources that have already been posted, Chapter 9 of Jurafsky and Martin's "Speech and Language Processing" has a nice overview of attention, and the next chapter talks specifically about the Transformer architecture: https://web.stanford.edu/~jurafsky/slp3/
https://udlbook.github.io/udlbook/ (https://news.ycombinator.com/item?id=38424939)
https://fleuret.org/francois/lbdl.html (https://news.ycombinator.com/item?id=35767789)
https://www.fast.ai/ (https://news.ycombinator.com/item?id=24237207)
https://d2l.ai/ (https://news.ycombinator.com/item?id=38428225)
Some more:
https://news.ycombinator.com/item?id=35543774
There is a lot more. Just google for "deep learning", and you'll find a lot of content. And most of that will cover attention, as it is a really basic concept now.