Variational Methods for Machine Learning with Applications to Deep Networks (Record no. 78466)
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| fixed length control field | 04022nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-030-70679-1 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801220337.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210510s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783030706791 |
| -- | 978-3-030-70679-1 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382 |
| 100 1# - AUTHOR NAME | |
| Author | Cinelli, Lucas Pinheiro. |
| 245 10 - TITLE STATEMENT | |
| Title | Variational Methods for Machine Learning with Applications to Deep Networks |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIV, 165 p. 54 illus., 33 illus. in color. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Fundamentals of Statistical Inference -- Model-Based Machine Learning and Approximate Inference -- Bayesian Neural Networks -- Variational Autoencoders -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Marins, Matheus Araújo. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Barros da Silva, Eduardo Antônio. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Netto, Sérgio Lima. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-030-70679-1 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 336 ## - | |
| -- | text |
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| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Telecommunication. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Communications Engineering, Networks. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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