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008			191101s2020 gw a o |||| 0|eng d
020			_a9783030317645
024	7		_a10.1007/978-3-030-31764-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
245	0	0	_aDevelopment and Analysis of Deep Learning Architectures _cedited by Witold Pedrycz, Shyi-Ming Chen.
250			_a1st ed. 2020.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020.
300			_a1 recurso en línea (XI, 292 páginas) _b135 ilustraciones, 120 ilustraciones a color.
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v867
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aPreface -- Chapter 1. Direct Error Driven Learning for Classification in Applications Generating Big-Data -- Chapter 2. Deep Learning for Soft Sensor Design -- Chapter 3. Case Study: Deep Convolutional Networks in Healthcare, etc.
520	3		_aThis book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
988			_aPrimersemestre_2020_Robotics
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aPedrycz, Witold _d1953- _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _967495
700	1		_aChen, Shyi-Ming _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _998849
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030317638
776	0	8	_iPrinted edition: _z9783030317652
776	0	8	_iPrinted edition: _z9783030317669
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-31764-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _ek _zSI