Fun Bayesian Stats for ML

I'm always looking for alternative ways of explaining the most important ideas in ML for myself and my teammembers/students. I came across this book by @willkurt that I think fits that need.

The nice thing about this book is it is rigorous enough to not send you in the wrong direction once you mastered the topic and decide to dig deeper in the literature.

Here is an example explaining the Kullback-Leibler Divergence a key concept when training CNNs.

And here is a link to the Amazon listing of the book.