Variational methods machine learning par cinelli lucas (17 résultats)
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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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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EUR 148,90
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Etat : New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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Etat : New.
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Vendeur : preigu, Osnabrück, Allemagnepreigu
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Taschenbuch. Etat : Neu. Variational Methods for Machine Learning with Applications to Deep Networks | Lucas Pinheiro Cinelli (u. a.) | Taschenbuch | xiv | Englisch | 2022 | Springer | EAN 9783030706814 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springe…r[dot]com | Anbieter: preigu.
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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Taschenbuch. Etat : 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 a…nd 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.
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Vendeur : Brook Bookstore On Demand, Napoli, NA, ItalieBrook Bookstore On Demand
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Etat : new. Questo è un articolo print on demand.
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Vendeur : Brook Bookstore On Demand, Napoli, NA, ItalieBrook Bookstore On Demand
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Etat : new. Questo è un articolo print on demand.
Langue : anglais
Edité par Springer International Publishing Mai 2022, 2022
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Etat : 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 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. 180 pp. Englisch.
Langue : anglais
Edité par Springer International Publishing Mai 2021, 2021
- Couverture rigide
- impression à la demande
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 106,99
EUR 23,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Buch. Etat : 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.
Variational Methods for Machine Learning with Applications to Deep Networks
Lucas Pinheiro Cinelli|Matheus Araújo Marins|Eduardo Antônio Barros da Silva|Sérgio Lima Netto
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Vendeur : moluna, Greven, Allemagnemoluna
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EUR 92,27
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Gebunden. Etat : 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 aspect…s, and pseudo-codesEvery chap.
Variational Methods for Machine Learning with Applications to Deep Networks
Cinelli, Lucas Pinheiro|Marins, Matheus Araújo|Barros da Silva, Eduardo Antônio|Netto, Sérgio Lima
Langue : anglais
Edité par Springer, Berlin|Springer International Publishing|Springer, 2022
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Vendeur : moluna, Greven, Allemagnemoluna
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Kartoniert / Broschiert. Etat : 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 Learnin…g, 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
- Couverture rigide
- impression à la demande
Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 155,83
EUR 7,63 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 4 disponible(s)
Etat : 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
- Couverture souple
- impression à la demande
Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
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EUR 156,06
EUR 7,63 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 4 disponible(s)
Etat : 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
- Couverture rigide
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Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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EUR 155,68
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Etat : 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
- Couverture souple
- impression à la demande
Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 156,07
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Etat : New. PRINT ON DEMAND.
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Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 95,70
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Buch. Etat : 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 | An…bieter: preigu Print on Demand.
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 106,99
EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : 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 Graphi…cal 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.
- Couverture rigide
- impression à la demande
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 106,99
EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Buch. Etat : 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 Mod…els 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.
