Normalization Techniques in Deep Learning
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Beschreibung
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.
In addition, this book:
- Presents a research landscape for normalization techniques, including methods, analysis, and applications
- Features normalization methods that improve the training stability, optimization efficiency, and generalization of DNNs
- Provides valuable guidelines for selecting normalization techniques to use in training DNNs for various applications
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.
Presents a research landscape for normalization techniques, including methods, analysis, and applications Features normalization methods that improve the training stability, optimization efficiency, and generalization of DNNs Provides valuable guidelines for selecting normalization techniques to use in training DNNs for various applications
Autor*in
Lei Huang
Themen in »Normalization Techniques in Deep Learning«
Deep Neural Networks (DNNs) Normalization Techniques Optimization Statistical Learning Natural Language Processing (NLP) Batch Normalization Weight Normalization Image Classifcation Domain Adaptation Image Style Transfer Generative Adversarial Networks