Normalization Techniques in Deep Learning
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Presents valuable guidelines for selecting normalization techniques for use in training deep neural networks Discusses the research landscape of normalization techniques and covers the needed methods, analysis, and applications Features normalization methods that improve the training stability, optimization efficiency, and generalization of DNNs
Autor*in
Lei Huang
Themen in »Normalization Techniques in Deep Learning«
Computer Vision Deep Neural Networks (DNNs) Normalization Techniques Machine Learning Artificial Intelligence Optimization Statistical Learning Natural Language Processing (NLP) Batch Normalization Weight Normalization Image Classifcation Domain Adaptation Image Style Transfer Generative Adversarial Networks