Verwandte Artikel zu Normalization Techniques in Deep Learning (Synthesis...

Normalization Techniques in Deep Learning (Synthesis Lectures on Computer Vision) - Hardcover

Huang, Lei

 
9783031145940: Normalization Techniques in Deep Learning (Synthesis Lectures on Computer Vision)
Hardcover
ISBN 10: 3031145941 ISBN 13: 9783031145940
Verlag: Springer, 2022

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

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.

Von der hinteren Coverseite

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Springer
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
3031145941
ISBN 13 
9783031145940
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
121
Kontakt zum Hersteller
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3,69115 Heidelberg, Germany
Heidelberg
69115
Deutschland

Weitere beliebte Ausgaben desselben Titels

9783031145971: Normalization Techniques in Deep Learning (Synthesis Lectures on Computer Vision)

Vorgestellte Ausgabe

ISBN 10:  3031145976 ISBN 13:  9783031145971
Verlag: Springer, 2023
Softcover