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008			220212s2021 sz | s |||| 0|eng d
020			_a9783030706791
024	7		_a10.1007/978-3-030-70679-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aCinelli, Lucas Pinheiro _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9681961
245	1	0	_aVariational Methods for Machine Learning with Applications to Deep Networks _cby Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XIV, 165 páginas) _b 54 ilustraciones, 33 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aIntroduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion.
520	3		_aThis book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.
988			_aSpringer_Engineering_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9160470 _aEstadística bayesiana
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aMarins, Matheus Araújo _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9681962
700	1		_aBarros da Silva, Eduardo Antônio _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9681963
700	1		_aNetto, Sérgio Lima _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9681964
776	0	8	_iPrinted edition: _z9783030706784
776	0	8	_iPrinted edition: _z9783030706807
776	0	8	_iPrinted edition: _z9783030706814
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-70679-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eIG _zSI