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Beschreibung
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.
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.
Über den Autor

Liu Peng is currently an Assistant Professor of Quantitative Finance at the Singapore Management University (SMU). His research interests include generalization in deep learning, sparse estimation, Bayesian optimization.

Inhaltsverzeichnis

1. Unveiling Generalization in Deep Learning 2. Introduction to Statistical Learning Theory 3. Classical Perspectives on Generalization 4. Modern Perspectives on Generalization 5. Fundamentals of Deep Neural Networks 6. A Concluding Perspective

Details
Erscheinungsjahr: 2025
Fachbereich: Datenkommunikation, Netze & Mailboxen
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781032841892
ISBN-10: 1032841893
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Peng, Liu
Hersteller: Chapman and Hall/CRC
Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, D-80331 München, gpsr@taylorandfrancis.com
Maße: 234 x 156 x 13 mm
Von/Mit: Liu Peng
Erscheinungsdatum: 12.09.2025
Gewicht: 0,36 kg
Artikel-ID: 133912255