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040			_aES-MaUEC _bspa _cES-MaUEC
336			_aTexto _btxt _2rdacontent
988			_aSpringerBiomedLife_2019
999			_c109253 _d109253 _x1
001			109253
005			20240111050150.0
008			190215s2019 gw | o |||| 0|eng d
020			_a9783030060732
024	7		_a10.1007/978-3-030-06073-2 _2doi
050		4	_aQA325.5 _b2019 EB
245	0	0	_aDeep Learning : _bFundamentals, Theory and Applications _cedited by Kaizhu Huang, Amir Hussain, Qiu-Feng Wang, Rui Zhang
264		1	_aCham, Switzerland _bSpringer International Publishing _c2019
300			_a1 recurso en línea (VII, 163 páginas) _b66 ilustraciones, 46 ilustraciones a color
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aBiomedical and Life Sciences (Springer-11642)
490	0		_aCognitive Computation Trends _x2524-5341 _v2
505	0		_aPreface -- Introduction to Deep Density Models with Latent Variables -- Deep RNN Architecture: Design and Evaluation -- Deep Learning Based Handwritten Chinese Character and Text Recognition -- Deep Learning and Its Applications to Natural Language Processing -- Deep Learning for Natural Language Processing -- Oceanic Data Analysis with Deep Learning Models -- Index.
520	3		_aThe purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aHuang, Kaizhu. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHussain, Amir _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _986026
700	1		_aWang, Qiu-Feng. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aZhang, Rui. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030060725
776	0	8	_iPrinted edition: _z9783030060749
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi-org/10.1007/978-3-030-06073-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b07/2019 _ek _zSI