Variational Methods for Machine Learning with Applications to Deep Networks

Language: English

Published by Springer, Springer Mai 2022, 2022

3030706818 / 9783030706814

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch. …

Seller Inventory # 9783030706814

Title
Variational Methods for Machine Learning with Applications to Deep Networks
Author
Lucas Pinheiro Cinelli
Publisher
Springer, Springer Mai 2022
Publication year
2022
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3030706818
ISBN 13
9783030706814
Item weight
316 grams
Dimensions
235x155x10 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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