The Principles of Deep Learning Theory

Boris Hanin

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Language: English

Published by Cambridge University Press, 2022

1316519333 / 9781316519332

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781316519332

Title
The Principles of Deep Learning Theory
Author
Boris Hanin
Publisher
Cambridge University Press
Publication year
2022
Condition
Neu
Binding
Buch
Language
English
ISBN 10
1316519333
ISBN 13
9781316519332
Item weight
1,075 grams
Dimensions
260x183x30 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

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