This one is very good, and will provide certain key insights on the way you should think at NNs. -> https://www.amazon.it/Deep-Learning-Python-Francois-Chollet/...

This is a good explanation of the Transformer details -> https://www.youtube.com/watch?v=bCz4OMemCcA&ab_channel=UmarJ...

This is old but covers a lot of background that you needs to know to understand very well the rest. What I like of this book is that it often explains in a very intuitive way the motivations behind certain choices. -> https://www.amazon.it/Natural-Language-Processing-Pytorch-Ap...

The 2nd edition of Deep Learning with Python[1] (by François Chollet of Keras fame) has a pretty good chapter on the transformer architecture[2]. In fact, the explanations in the book are clear and lucid across the board.

[1]: https://www.amazon.com/Deep-Learning-Python-Francois-Chollet...

[2]: https://en.wikipedia.org/wiki/Transformer_(machine_learning_...

The ones I really enjoyed reading were:

https://www.pyimagesearch.com/deep-learning-computer-vision-...

https://www.machinelearningisfun.com/

and

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet...

Thank you for the tips.

The Deep Learning Book (http://deeplearningbook.org) was one of my main studying materials. How would you compare the other DL book you mentioned (https://www.amazon.com/Deep-Learning-Python-Francois-Chollet...) against this one?