Normalization Techniques in Deep Learning (Record no. 85673)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03285nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-14595-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730164430.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221008s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031145957 |
| -- | 978-3-031-14595-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.37 |
| 100 1# - AUTHOR NAME | |
| Author | Huang, Lei. |
| 245 10 - TITLE STATEMENT | |
| Title | Normalization Techniques in Deep Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 110 p. 26 illus., 21 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Computer Vision, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Motivation and Overview of Normalization in DNNs -- A General View of Normalizing Activations -- A Framework for Normalizing Activations as Functions -- Multi-Mode and Combinational Normalization -- BN for More Robust Estimation -- Normalizing Weights -- Normalizing Gradients -- Analysis of Normalization -- Normalization in Task-specific Applications -- Summary and Discussion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-14595-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2022. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) . |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Vision. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical Models of Cognitive Processes and Neural Networks. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2153-1064 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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