Araujo netto (22 risultati)

Soft cover. Condizione: Poor. Softcover book of 403 pages in Portuguese, including section of photos; Very Good condition, pages clean and binding square and tight.

Encuadernación de tapa blanda. Condizione: Aceptable. 24 x 24 cm. Capas com desgastes. Lombada com perda de material. Foxing spots em algumas páginas. Some texts in english, portuguese and french.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Condizione: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Hardback. Condizione: 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 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.…

Hardcover. Condizione: Brand New. 179 pages. 9.25x6.10x0.55 inches. In Stock.

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

Condizione: New.

Condizione: 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.

Hardback. Condizione: 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 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.…

Tapa Blanda. Condizione: New. O presente livro procura focalizar, além das referências históricas da formação da disciplina, seus conceito, definições, princípios e abrangência; as pessoas no direito administrativo; os serviços públicos e sua titularidade; a administração, suas estruturas; o regime jurídico dos bens públicos; os mecanismos de controle da legalidade da administração, e, aposição da administração em juízo. 1268 Páginas. 1650 gr. Libro.…

Kartoniert / Broschiert. Condizione: 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.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Condizione: New. PRINT ON DEMAND.

Condizione: New. WE CAN PROVIDE ANY BOOK FROM BRAZIL ! ---- All our books are Shipped with Fedex or the Brasilian Post for standard Shipment, Fedex or DHL for expedited shipment. ---- For more information, please contact us. ---- The book will be sent from Brazil. ----- This is a Print on Demand book (POD).…