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Variational methods for machine lear...

Cinelli, Lucas Pinheiro.

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Variational methods for machine learning with applications to deep networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Variational methods for machine learning with applications to deep networks/ by Lucas Pinheiro Cinelli ... [et al.].
other author:	Cinelli, Lucas Pinheiro.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiv, 165 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-70679-1
ISBN:	9783030706791

Variational methods for machine learning with applications to deep networks
Variational methods for machine learning with applications to deep networks[electronic resource] /by Lucas Pinheiro Cinelli ... [et al.]. - Cham :Springer International Publishing :2021. - xiv, 165 p. :ill., digital ;24 cm.

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:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Variational methods for machine learning with applications to deep networks
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245 0 0 $a Variational methods for machine learning with applications to deep networks $h [electronic resource] / $c by Lucas Pinheiro Cinelli ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 165 p. : $b ill., digital ; $c 24 cm.
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 Machine learning. $3 533906
650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Cinelli, Lucas Pinheiro. $3 3495750
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-70679-1
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