Isbn: 9783030610807 - computational methods for deep learning: theoretic, practice and applications (texts in computer science) (7 resultados)

Idioma: Inglés
Editorial: Springer, 2020
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 53,55
Envío por EUR 3,47Se envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Idioma: Inglés
Editorial: Springer, 2020
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 51,05
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Condición: New.

Idioma: Inglés
Editorial: Springer, 2020
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 52,09
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Condición: New.

Idioma: Inglés
Editorial: Springer, Berlin, Springer International Publishing, Springer, 2020
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 93,36
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations.Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms.As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers.This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision.Dr. Wei Qi Yanis an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title,Visual Cryptography for Image Processing and Security.…

Idioma: Inglés
Editorial: Springer International Publishing, 2020
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Librería: Buchpark, Trebbin, AlemaniaBuchpark
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EUR 45,27
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Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Idioma: Inglés
Editorial: Springer International Publishing, 2020
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Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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EUR 179,00
Envío por EUR 39,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: gut. 2020. Computational Methods for Deep Learning In deutscher Sprache. pages.

Idioma: Inglés
Editorial: Berlin Springer International Publishing Springer Dez 2020, 2020
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- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 64,19
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations.Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms.As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers.This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision.Dr. Wei Qi Yanis an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title,Visual Cryptography for Image Processing and Security. 134 pp. Englisch.…