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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks - Hardcover
Inhaltsangabe
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
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Über die Autorin bzw. den Autor
Daniel A. Roberts was cofounder and CTO of Diffeo, an AI company acquired by Salesforce; a research scientist at Facebook AI Research; and a member of the School of Natural Sciences at the Institute for Advanced Study in Princeton, NJ. He was a Hertz Fellow, earning a PhD from MIT in theoretical physics, and was also a Marshall Scholar at Cambridge and Oxford Universities.
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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
Verlag:
Cambridge University Press, 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
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Hardback. Zustand: Good. 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. Bestandsnummer des Verkäufers CIN1316519333G
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Beispielbild für diese ISBN
The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
Verlag:
Cambridge University Press, 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
Gebraucht
Hardcover
Anbieter: World of Books Inc, Montgomery, IL, USA
Verkäuferbewertung 4 von 5 Sternen
Hardback. Zustand: Good. 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. Bestandsnummer des Verkäufers CIN1316519333G
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Beispielbild für diese ISBN
The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
Verlag:
Cambridge University Press, 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
Gebraucht
Hardcover
Anbieter: World of Books (was SecondSale), Montgomery, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Hardback. Zustand: Very Good. 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. Bestandsnummer des Verkäufers CIN1316519333VG
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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
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Cambridge University Press (edition New), 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
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Hardcover. Zustand: Very Good. New. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Bestandsnummer des Verkäufers 1316519333-9-1-NAU
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Beispielbild für diese ISBN
The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
Verlag:
Cambridge University Press, 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
Gebraucht
Hardcover
Anbieter: World of Books Inc, Montgomery, IL, USA
Verkäuferbewertung 4 von 5 Sternen
Hardback. Zustand: Very Good. 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. Bestandsnummer des Verkäufers CIN1316519333VG
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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
Verlag:
Cambridge University Press (edition New), 2022
ISBN 10: 1316519333
ISBN 13: 9781316519332
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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
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Principles of Deep Learning Theory : An Effective Theory Approach to Understanding Neural Networks
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ISBN 13: 9781316519332
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