Normalization Techniques in Deep Learning - Couverture rigide

Huang, Lei

Couverture rigide

ISBN 10 : 3032199905 ISBN 13 : 9783032199904

Editeur : Springer Nature Switzerland AG, 2026

Synopsis

This book surveys normalization techniques with a deep analysis in training deep neural networks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs. This Second Edition builds upon the original material with the addition of more recent proposed methods and expanded technical details for new normalization methods and network architectures tailored to specific tasks.

A propos du livre

À propos de l'auteur

Lei Huang, Ph.D., is an Associate Professor at Beihang University. His current research interests include normalization techniques involving methods, theories, and applications in training deep neural networks (DNNs). He also has wide interests in representation and optimization of deep learning theory and computer vision tasks. Dr. Huang serves as a reviewer for top-tier conferences and journals in machine learning and computer vision.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3032199905
• ISBN 13: 9783032199904
• Reliure: Relié
• Numéro d'édition: 2
• Nombre de pages: 180
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne