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

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

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Deep Learning

Neural Networks

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ISBN-10
1316519333
ISBN-13
978-1316519332
Publisher
Cambridge University Press
Price
75.99
File Type
PDF
Page No.
472
Sho Yaida

Sho Yaida

Sho Yaida (https://shoyaida.com) is currently a Research Scientist at Meta AI. Prior to joining Meta ...
Daniel A. Roberts

Daniel A. Roberts

Dan Roberts (https://danintheory.com) is currently a Research Affiliate at the Center for Theoretica ...

Review

'In the history of science and technology, the engineering artifact often comes first: the telescope, the steam engine, digital communication. The theory that explains its function and its limitations often appears later: the laws of refraction, thermodynamics, and information theory. With the emergence of deep learning, AI-powered engineering wonders have entered our lives but our theoretical understanding of the power and limits of deep learning is still partial. This is one of the first books devoted to the theory of deep learning, and lays out the methods and results from recent theoretical approaches in a coherent manner.' Yann LeCun, New York University and Chief AI Scientist at Meta

'For a physicist, it is very interesting to see deep learning approached from the point of view of statistical physics. This book provides a fascinating perspective on a topic of increasing importance in the modern world.' Edward Witten, Institute for Advanced Study

'This is an important book that contributes big, unexpected new ideas for unraveling the mystery of deep learnings effectiveness, in unusually clear prose. I hope it will be read and debated by experts in all the relevant disciplines.' Scott Aaronson, University of Texas at Austin

'It is not an exaggeration to say that the world is being revolutionized by deep learning methods for AI. But why do these deep networks work? This book offers an approach to this problem through the sophisticated tools of statistical physics and the renormalization group. The authors provide an elegant guided tour of these methods, interesting for experts and non-experts alike. They write with clarity and even moments of humor. Their results, many presented here for the first time, are the first steps in what promises to be a rich research program, combining theoretical depth with practical consequences.' William Bialek, Princeton University

'This books physics-trained authors have made a cool discovery, that feature learning depends critically on the ratio of depth to width in the neural net.' Gilbert Strang, Massachusetts Institute of Technology

Book Description

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

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