Normalization Techniques in Deep Learning - Couverture souple

Huang, Lei

Couverture souple

ISBN 10 : 3031145976 ISBN 13 : 9783031145971

Editeur : Springer, 2023

Synopsis

?This book presents and surveys normalization techniques with a deep analysis in training deep neural networks.  In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks.  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 tasks.  The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.

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.

À propos de la quatrième de couverture

This book presents and surveys normalization techniques with a deep analysis in training deep neural networks.  In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks.  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 tasks.  The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 3031145976
• ISBN 13: 9783031145971
• Reliure: Broché
• Nombre de pages: 110
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible