For more modern material, there are a few new good books on Transformers. Transformers are interesting because they were designed for efficiency: layers the same size, encoding both data and time sequencing information in each sample (so recurrent NNs aren’t required), etc.
For a higher-level conceptual view of how Transformers work, you can check out the now-classic "Illustrated Transformer" series [3] and this programmer-oriented explanation (with code in Rust) from someone at Anthropic [4].
[1] https://www.oreilly.com/library/view/natural-language-proces...
[2] https://github.com/karpathy/minGPT
[3] https://jalammar.github.io/illustrated-transformer/
[4] https://blog.nelhage.com/post/transformers-for-software-engi...