Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory

This book aims to demystify large deep networks and generative models, often perceived as "black boxes," by exploring their internal mechanisms through the lens of representation learning. It outlines how modern neural network architectures are designed, utilizing optimization and information theory.