9783030706814 - variational methods for machine learning with applications to deep networks de cinelli, lucas pinheiro; marins, matheus araújo; barros da silva, eduardo antônio; netto, sérgio lima (9 resultados)
Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio; Netto, Sérgio Lima
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 148,44
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 106,99
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Mode…ls 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.
Variational Methods for Machine Learning with Appl
Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio; Netto, Sérgio Lima
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 93,41
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Condición: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 86,24
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Condición: new. Questo è un articolo print on demand.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 106,99
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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 Probabilisti…c 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. 180 pp. Englisch.
Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro|Marins, Matheus Araújo|Barros da Silva, Eduardo Antônio|Netto, Sérgio Lima
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer, 2022
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Librería: moluna, Greven, Alemaniamoluna
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EUR 92,27
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 Lea…rning, the authors motivate Probabilistic Graphical Models and sh.
Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio; Netto, Sérgio Lima
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 155,57
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Condición: New. Print on Demand.
Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio; Netto, Sérgio Lima
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 156,07
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Condición: New. PRINT ON DEMAND.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 106,99
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. 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 Gr…aphical 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.
