Deep Learning Generalization - Theoretical Foundations and Practical Strategies

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.

List of contents

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

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.

Product details

• Authors: Liu Peng, Peng Liu
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 27.06.2025
• EAN: 9781032841892
• ISBN: 978-1-0-3284189-2
• No. of pages: 220
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks; Optimization, MATHEMATICS / Complex Analysis, Probability & statistics, Mathematical & statistical software, Information technology: general issues, Probability and statistics, Information technology: general topics, COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Machine Learning, Mathematical and statistical software, Neural networks and fuzzy systems, Neural networks & fuzzy systems