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.

Table des matières

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

A propos de l'auteur

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.

Détails du produit

• Auteurs: Liu Peng, Peng Liu
• Edition: Taylor & Francis Ltd.
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 27.06.2025
• EAN: 9781032841908
• ISBN: 978-1-0-3284190-8
• Pages: 200
• Catégories: Sciences naturelles, médecine, informatique, technique > Mathématiques > Autres; 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