Normalization Techniques in Deep Learning



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Springer


Paru le : 2022-10-08



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Description

?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.
Pages
110 pages
Collection
n.c
Parution
2022-10-08
Marque
Springer
EAN papier
9783031145940
EAN PDF
9783031145957

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
2908 Ko
Prix
58,01 €
EAN EPUB
9783031145957

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
10926 Ko
Prix
58,01 €

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.

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