Geometric Foundations of Deep Learning
As you start diving into the theoritical details, deep learning neural networks are perhaps not so mysterious after all. It helps to have a good understanding of non-linear function approximation, manifolds, and gauge theory. The material below will help out.
Great article by Michael Bronstein on the geometric foundations of deep learning can be found here. We featured Michael's ICLR keynote talk here.
It's a summary of a more comprehensive 'proto-book' paper that can be found here.
We're going to feature Petar Velickovic's talk on this research here. He's one of the co-authors on the paper above.