1st Edition
Deep Learning Generalization Theoretical Foundations and Practical Strategies
By Liu Peng
Copyright 2025
230 Pages
62 B/W Illustrations
by
Chapman & Hall
230 Pages
62 B/W Illustrations
by
Chapman & Hall
230 Pages
62 B/W Illustrations
by
Chapman & Hall
Also available as eBook on:
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... Read more
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
Biography
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.
