I think it would be extremely helpful to map the math into code. Nobody has done this as far as I've seen. I mean you can find Github repositories for some papers but really a set of explicit tutorials from the math to the code would be really helpful.

To say something is "machine learning", I think means that you should show the code not just equations and derivations.

I mean if you only show math and derivations, what's the point? To show off what you know? How is that helpful?

This exists actually, it's not complete yet (I think?) but it covers a lot of the material in the book:

https://github.com/probml/pyprobml

Yeh at least he gives a full derivation for every equation :)