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020			_a9783030931582
024	7		_a10.1007/978-3-030-93158-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.624 _b2022 EB
100			_aTomczak, Jakub M _4http://id.loc.gov/vocabulary/relators/aut _9683350 _cDeep generative modeling
245	1	0	_aDeep Generative Modeling _cby Jakub M. Tomczak
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XVIII, 197 páginas) _b127 ilustraciones, 122 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
505	0		_aWhy Deep Generative Modeling? -- Autoregressive Models -- Flow-based Models -- Latent Variable Models -- Hybrid Modeling -- Energy-based Models -- Generative Adversarial Networks -- Deep Generative Modeling for Neural Compression -- Useful Facts from Algebra and Calculus -- Useful Facts from Probability Theory and Statistics -- Index.
520			_aThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.
988			_aSpringer_Computer_2022
650		7	_2embne _aProgramación de ordenadores _9139821
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030931575
776	0	8	_iPrinted edition: _z9783030931599
776	0	8	_iPrinted edition: _z9783030931605
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-93158-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _eh _zSI