Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
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Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Language: English
Published by Chapman and Hall/CRC -, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
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paperback. Condition: New.
Language: English
Published by Taylor & Francis Ltd, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
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Condition: New.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 200 pages. 9.18x6.12x9.21 inches. In Stock.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Deep Learning Generalization | Theoretical Foundations and Practical Strategies | Liu Peng | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032841892 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841907 ISBN 13: 9781032841908
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841907 ISBN 13: 9781032841908
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841907 ISBN 13: 9781032841908
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032841907 ISBN 13: 9781032841908
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 200 pages. 9.18x6.12x9.45 inches. In Stock.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 200 pages. 9.18x6.12x9.21 inches. In Stock. This item is printed on demand.
Language: English
Published by Chapman And Hall/CRC, 2025
ISBN 10: 1032841893 ISBN 13: 9781032841892
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization.The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes.By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications.For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning.
Language: English
Published by Chapman And Hall/CRC, 2025
ISBN 10: 1032841907 ISBN 13: 9781032841908
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.