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"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"-- |
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Roberts, Daniel A. 1987- VerfasserIn (DE-588)1261776224 (DE-627)1809078989 aut, The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts (MIT), Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University), Cambridge New York, NY Port Melbourne, VIC New Delhi Singapore Cambridge University Press 2022, x, 460 Seiten Diagramme, Text txt rdacontent, ohne Hilfsmittel zu benutzen n rdamedia, Band nc rdacarrier, Includes bibliographical references and index, "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"--, Deep learning (Machine learning), SCIENCE / Physics / Mathematical & Computational, s (DE-588)1135597375 (DE-627)890512922 (DE-576)489847412 Deep learning gnd, (DE-627), Yaida, Sho VerfasserIn (DE-588)1261764668 (DE-627)1809045266 aut, Hanin, Boris MitwirkendeR (DE-588)1261776534 (DE-627)1809079454 ctb, 9781009023405 epub, Erscheint auch als Online-Ausgabe Roberts, Daniel A., 1987 - The principles of deep learning theory Cambridge : Cambridge University Press, 2022 1 online resource (x, 460 pages) (DE-627)1807444929 9781009023405, https://zbmath.org/1507.68003 zbMATH Rezension |
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Roberts, Daniel A., Yaida, Sho, The principles of deep learning theory: an effective theory approach to understanding neural networks, "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"--, Deep learning (Machine learning), SCIENCE / Physics / Mathematical & Computational, Deep learning |
| title |
The principles of deep learning theory: an effective theory approach to understanding neural networks |
| title_auth |
The principles of deep learning theory an effective theory approach to understanding neural networks |
| title_full |
The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts (MIT), Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University) |
| title_fullStr |
The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts (MIT), Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University) |
| title_full_unstemmed |
The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts (MIT), Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University) |
| title_short |
The principles of deep learning theory |
| title_sort |
the principles of deep learning theory an effective theory approach to understanding neural networks |
| title_sub |
an effective theory approach to understanding neural networks |
| title_unstemmed |
The principles of deep learning theory: an effective theory approach to understanding neural networks |
| topic |
Deep learning (Machine learning), SCIENCE / Physics / Mathematical & Computational, Deep learning |
| topic_facet |
Deep learning (Machine learning), SCIENCE / Physics / Mathematical & Computational, Deep learning |
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https://zbmath.org/1507.68003 |
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