Netto sergio (15 Ergebnisse)

Zustand: Very Good. 1st. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Zustand: Très bon. Ancien livre de bibliothèque avec équipements. Edition 2010. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2010. Ammareal gives back up to 15% of this item's net price to charity organizations.…

Neritica, Curitiba, v. 10, Editora da UFPR, 1997, 16 pages agrafées de 14,50x21 cm Très bon état - Pour les envois hors de France, la tafication «livre & brochure» pour les frais de port a disparue.Les frais de port annoncés correspondent à une moyenne. Ils seront calculés au plus juste en fonction du poids de votre article.…

Zustand: As New. : Este libro es una edición revisada que cubre los principales temas del diseño y análisis del procesamiento de señales digitales (DSP) en un solo volumen. Integra la teoría con ejemplos del mundo real y compensaciones de diseño. Incluye material nuevo sobre el procesamiento de señales aleatorias, un capítulo sobre la estimación espectral, una cobertura ampliada de los bancos de filtros y las ondículas, y material sobre la solución de ecuaciones de diferencia. Con 120 ejemplos resueltos, 20 estudios de caso y casi 400 ejercicios, este libro es una lectura esencial para cualquier persona que tome cursos de DSP. EAN: 9780521887755 Tipo: Libros Categoría: Tecnología|Educación Título: Digital Signal Processing Autor: Paulo S. R. Diniz| Eduardo A. B. da Silva| Sergio L. Netto Editorial: Cambridge University Press Idioma: en Páginas: 912 Formato: tapa dura.…

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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

Zustand: New. In English.

Zustand: Gut. Zustand: Gut | Seiten: 632 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: New. In English.

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

Zustand: 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.

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.…

Hardcover. Zustand: Brand New. 2nd edition. 912 pages. 9.80x7.70x1.90 inches. In Stock.

Zustand: New.