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020 _a9783030706791
_9978-3-030-70679-1
024 7 _a10.1007/978-3-030-70679-1
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
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082 0 4 _a621.382
_223
100 1 _aCinelli, Lucas Pinheiro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949764
245 1 0 _aVariational Methods for Machine Learning with Applications to Deep Networks
_h[electronic resource] /
_cby Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXIV, 165 p. 54 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion.
520 _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.
650 0 _aTelecommunication.
_910437
650 0 _aMachine learning.
_91831
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_949765
700 1 _aMarins, Matheus Araújo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949766
700 1 _aBarros da Silva, Eduardo Antônio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949767
700 1 _aNetto, Sérgio Lima.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949768
710 2 _aSpringerLink (Online service)
_949769
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030706784
776 0 8 _iPrinted edition:
_z9783030706807
776 0 8 _iPrinted edition:
_z9783030706814
856 4 0 _uhttps://doi.org/10.1007/978-3-030-70679-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78466
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