[maxbond]

For anyone who can code and wants to get up and running with neural nets quickly, with a focus on getting your hands dirty over understanding the theory (I'm finding the former is leading naturally to the latter), I recommend Deep Learning with Python by Francois Chollet.

(If anyone has suggestions for a book to read after that one, I'm all ears.)

[xrd]

This book was published in 2017. Is it still relevant? I would only say that because the pace of the last six months feels like we are time traveling. :)

[maxbond]

Totally reasonable question.

The second edition was published in 2021. It's a book for beginners, so I think it's fine as far as relevance goes. It doesn't cover the attention mechanism, which is a weakness, but ultimately it seems to me like you're gunnuh have to hit the papers at some point to learn deep learning. For me this book spanned the gap until I could follow the papers (at least, the basic ones, I'm still pretty early in my learning). Having enough Keras to be able to try stuff and try different techniques on the same dataset has also been tremendously helpful. (You might also say that one should learn PyTorch instead, and that's fair.)

Luckily many papers start with a verbose preamble explaining the history and motivation for their approach, which is annoying when you're experienced, but helpful for a beginner.

[xrd]

Great, I obviously didn't look long enough to see there was a second edition. Great feedback and I'll take your advice!

[maxbond]

Best of luck!