Deep Learning Generalization : Theoretical Foundations and Practical Strategies

Liu Peng

ISBN 10: 1032841907 / ISBN 13: 9781032841908

Published by Chapman And Hall/CRC, 2025

About this Item

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.

Seller Inventory # 9781032841908

Bibliographic Details

• Title: Deep Learning Generalization : Theoretical Foundations and Practical Strategies
• Publisher: Chapman And Hall/CRC
• Publication Date: 2025
• Language: English
• ISBN 10: 1032841907
• ISBN 13: 9781032841908
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 17D millimeters 240 millimeters width by 161 millimeters height by 17 millimeters depth
• Weight: 517 grams

About this title

Synopsis

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.

About the Author

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.