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Normalization Techniques in Deep Learning

By (author) Lei Huang

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normal price: R 1 365.95

Price: R 1 229.95


| book description |

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.

| product details |



Normally shipped | Forthcoming
Publisher | Springer Nature Switzerland AG
Published date | 9 Jun 2026
Language |
Format | Hardback
Pages | 154
Dimensions | 240 x 168 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-3-0321-9990-4
Readership Age |
BISAC |
Expected | 9 Jun 2026

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Normally shipped | Forthcoming. We are not accepting backorders for this item yet
Readership Age |
Normal Price | R 1 730.95
Price | R 1 557.95 | on special |



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