variational methods machine learning von cinelli lucas (17 Ergebnisse)

Zustand: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Zustand: New.

Taschenbuch. Zustand: Neu. Variational Methods for Machine Learning with Applications to Deep Networks | Lucas Pinheiro Cinelli (u. a.) | Taschenbuch | xiv | Englisch | 2022 | Springer Nature Switzerland | EAN 9783030706814 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.

Taschenbuch. Zustand: 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 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.

Buch. Zustand: 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 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.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Taschenbuch. Zustand: 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 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. 180 pp. Englisch.

Buch. Zustand: 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 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. 180 pp. Englisch.

Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep LearningPresents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codesEvery chap.

Kartoniert / Broschiert. Zustand: 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 Learning, the authors motivate Probabilistic Graphical Models and sh.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.

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Buch. Zustand: Neu. Variational Methods for Machine Learning with Applications to Deep Networks | Lucas Pinheiro Cinelli (u. a.) | Buch | xiv | Englisch | 2021 | Springer | EAN 9783030706784 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Taschenbuch. Zustand: 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 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.