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.

Info autore

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.

Dettagli sul prodotto

• Autori: Peng Liu, Liu Peng
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Copertina rigida
• Data pubblicazione: 27.06.2025
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Comunicazione dati, reti
• EAN: 9781032841908
• ISBN: 978-1-0-3284190-8
• Numero di pagine: 220
• Categorie: 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, statistical learning theory, overparameterization paradox, PyTorch implementation guide, overfitting prevention methods, model complexity control, neural tangent kernel, robust neural network generalization