國立虎尾科技大學 |

Variational Methods for Machine Learning with Applications to Deep Networks

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Variational Methods for Machine Learning with Applications to Deep Networks/ by Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto.
Author:	Cinelli, Lucas Pinheiro.
other author:	Marins, Matheus Araújo.
Description:	XIV, 165 p. 54 illus., 33 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-70679-1
ISBN:	9783030706791

Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro.

Variational Methods for Machine Learning with Applications to Deep Networks[electronic resource] /by Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto. - 1st ed. 2021. - XIV, 165 p. 54 illus., 33 illus. in color.online resource.

Introduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion.

This 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.

ISBN: 9783030706791

Standard No.: 10.1007/978-3-030-70679-1doiSubjects--Topical Terms:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Variational Methods for Machine Learning with Applications to Deep Networks
LDR:03115nam a22003975i 4500 001 1053993
003 DE-He213
005 20211124203418.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030706791 $9 978-3-030-70679-1
024 7 $a 10.1007/978-3-030-70679-1 $2 doi
035 $a 978-3-030-70679-1
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Cinelli, Lucas Pinheiro. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358954
245 1 0 $a Variational Methods for Machine Learning with Applications to Deep Networks $h [electronic resource] / $c by Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antônio Barros da Silva, Sérgio Lima Netto.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XIV, 165 p. 54 illus., 33 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion.
520 $a This 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 $a Electrical engineering. $3 596380
650 0 $a Machine learning. $3 561253
650 0 $a Computational intelligence. $3 568984
650 0 $a Data mining. $3 528622
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Marins, Matheus Araújo. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358955
700 1 $a Barros da Silva, Eduardo Antônio. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358956
700 1 $a Netto, Sérgio Lima. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358957
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030706784
776 0 8 $i Printed edition: $z 9783030706807
776 0 8 $i Printed edition: $z 9783030706814
856 4 0 $u https://doi.org/10.1007/978-3-030-70679-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)