Major update to draft of "Understanding Deep Learning" textbook is now available via:

udlbook.github.io/udlbook/

New material includes convolutional networks, residual connections, BatchNorm, transformers, and graph neural networks.

There's lots of stuff in here now that is rarely covered in other textbooks including double descent, implicit regularization, transformers for vision, why residual connections help, how BatchNorm works, graph attention networks, etc.

I learned a lot writing it and so probably you will reading it.

There are also lots of slides if you are teaching a course on Deep Learning this semester.

Feedback welcome! Thanks to all who have commented so far.