Computational Methods Deep Learning (106 results)
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Published by Springer, 2020
Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
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Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
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Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Rawat Publications, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer, 2021
Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Rawat Publications, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Karlsruher Institut Für Technologie, Karlsruher Institut Für Technologie, 2025
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering.
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Taschenbuch. Condition: Neu. Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images | Juliane Blarr | Taschenbuch | Englisch | 2025 | Karlsruher Institut für Technologie | EAN 9783731513964 | Verantwortliche Person… für die EU: KIT Scientific Publishing, Straße am Forum 2, 76131 Karlsruhe, info[at]ksp[dot]kit[dot]edu | Anbieter: preigu.
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Published by Springer, 2023
Series: Texts in Computer Science, Book 80 of 83. Book 80 of 83 - Texts in Computer Science
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Published by Springer International Publishing, 2021
Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
- Softcover
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Taschenbuch. Condition: 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 ma…chine 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.
Language: English
Published by Springer, 2023
Series: Lecture Notes on Numerical Methods in Engineering and Sciences, Book 8 of 8. Book 8 of 8 - Lecture Notes on Numerical Methods in Engineering and Sciences
- Softcover
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Condition: New. pp. 416.
Language: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2020
Series: Texts in Computer Science, Book 56 of 83. Book 56 of 83 - Texts in Computer Science
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 62.10
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Buch. Condition: 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 i…ntelligence. 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.
