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020			_a9783030610814
024	7		_a10.1007/978-3-030-61081-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aYan, Wei Qi _eautor _4http://id.loc.gov/vocabulary/relators/aut _998434
245	1	0	_aComputational Methods for Deep Learning : _bTheoretic, Practice and Applications _cby Wei Qi Yan
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVII, 134 páginas) _b23 ilustraciones, 22 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aTexts in Computer Science _x1868-0941
505	0		_a1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning.
520	3		_aIntegrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.
988			_aSpringer_Computer_2021
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aRedes neuronales artificiales _9678664
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9783030610807
776	0	8	_iPrinted edition: _z9783030610821
776	0	8	_iPrinted edition: _z9783030610838
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-61081-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _ek _zSI