The Principles of Deep Learning Theory

This book establishes a theoretical framework for deep learning by applying principles from statistical physics.

It offers clear explanations of how deep neural networks work, making novel results accessible to both theorists and practitioners.

The book is ideal for students and researchers in AI with minimal prerequisites in linear algebra, calculus, and probability theory.