9783030706784 - 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 (19 resultados)
Variational Methods for Machine Learning With Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araujo; da Silva, Eduardo Antonio Barros; Netto, Sergio Lima
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Variational Methods for Machine Learning With Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araujo; da Silva, Eduardo Antonio Barros; Netto, Sergio Lima
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Variational Methods for Machine Learning With Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araujo; da Silva, Eduardo Antonio Barros; Netto, Sergio Lima
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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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: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Variational Methods for Machine Learning With Applications to Deep Networks [Hardcover] [May 11, 2021] Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio and Netto, Sérgio Lima
Cinelli, Lucas Pinheiro; Marins, Matheus Araújo; Barros Da Silva, Eduardo Antônio; Netto, Sérgio Lima
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Librería: StainesBook, Weybridge, SURRE, Reino UnidoStainesBook
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Condición: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.
Variational Methods for Machine Learning With Applications to Deep Networks
Cinelli, Lucas Pinheiro; Marins, Matheus Araujo; da Silva, Eduardo Antonio Barros; Netto, Sergio Lima
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 179 pages. 9.25x6.10x0.55 inches. In Stock.
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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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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Librería: moluna, Greven, Alemaniamoluna
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EUR 106,22
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Condición: New. 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.
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- Edición internacional
Librería: UK BOOKS STORE, London, LONDO, Reino UnidoUK BOOKS STORE
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Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if th…e Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 106,99
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Buch. 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 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.
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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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 125,06
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Hardback. Condición: New. 2021 ed. 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 le…nds 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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Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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EUR 259,90
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Hardcover. Condición: gut. 2021. Variational Methods for Machine Learning with Applications to Deep Networks In deutscher Sprache. pages.
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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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Buch. 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 Probabilistic Graph…ical 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
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 148,40
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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 151,83
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Condición: New. PRINT ON DEMAND.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 95,70
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Buch. Condición: 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.
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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
Buch. 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 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.
