The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks

by Daniel A. Roberts, Sho Yaida, Boris Hanin

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.

Kindle Edition

Published May 26, 2022

About the author

I live with my wife in Oklahoma City, OK and I have two teenage daughters at home. I was originally born in Erie, PA and got moved to Florida when my parents retired in the middle of the 1970's.

I like to write to entertain readers as much as I like to write for fun. Creating worlds, people, various unique places for our imaginations to explore is my favorite hobby.