Mathematical theory of deep learning
It serves as an introductory guide for mathematicians and related fields, focusing on building understanding of essential concepts in deep learning theory.
The book introduces the mathematical analysis of deep learning, covering fundamental results in approximation, optimization, and statistical learning theory to provide foundational knowledge for students and researchers.
This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory. Serving as a guide for students and researchers in mathematics and related fields, the book aims to equip readers with foundational knowledge on the topic. It prioritizes simplicity over generality, and presents rigorous yet accessible results to help build an understanding of the essential mathematical concepts underpinning deep learning.