Overview
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.
The book, Normalization Techniques in Deep Learning (Synthesis Lectures on Computer Vision) [Bulk, Wholesale, Quantity] ISBN#9783031145940 in Hardcover by Lei Huang may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.
Details
Format:
Hardcover
Publication Date:
10/09/2022
ISBN-13:
9783031145940
Pages:
110
Publisher:
Springer International Publishing
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