Completely agree. However one thing that I think is missing in most books with a lot of math is the opportunity to use programming to help teach and communicate

Completely agree. However one thing that I think is missing in most books with a lot of math is the opportunity to use programming to help teach and communicate the math in question. A great example of a book that communicates abstract mathematical concepts via algorithms is the Little Schemer. Ironically, it doesn't even set out to communicate the math, but actually just uses the math to communicate other programming ideas like recursion. That, however, doesn't diminish the fact that it demonstrates ways to teach math through programming and algorithms. Another book which also does a good job at using programming to demonstrate more concrete math is Allen Downey's "Think Stats" book. All through the book, you learn the mathematical concepts of statistics through hands on programming activities. There definitely is a chicken and egg problem in areas like machine learning because unlike the above resources which have only one layer of abstraction to cross, machine learning presents two layers of programming<->math abstractions to cross for most people who decide to learn it. To really understand and apply machine learning you need to understand the math and models behind it. However the math and models are presented in pure form that makes it difficult to grok unless you arrived at the resource with a classically-trained mathematical background. I would hope that given that the target market for such learning resources it not mathematicians but programmers, that such learning resources would present content to help you arrive at the math from a programmer's point of view.