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008			221008s2022 sz | s |||| 0|eng d
020			_a9783031145957
024	7		_a10.1007/978-3-031-14595-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.73 _b2022 EB
100	1		_aHuang, Lei _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686605
245	1	0	_aNormalization Techniques in Deep Learning _cby Lei Huang
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XI, 110 páginas) _b26 ilustraciones, 21 ilustraciones en blanco y negro
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aIntroduction -- 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			_aThis 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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031145940
776	0	8	_iPrinted edition: _z9783031145964
776	0	8	_iPrinted edition: _z9783031145971
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-14595-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI