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Ajouter au panierEtat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Ajouter au panierEtat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Edité par Springer International Publishing, 2021
ISBN 10 : 3030610837 ISBN 13 : 9783030610838
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 64,19
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 73,80
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Edité par Springer International Publishing, Springer International Publishing Dez 2021, 2021
ISBN 10 : 3030610837 ISBN 13 : 9783030610838
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 64,19
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. 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 Yan is 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 152 pp. Englisch.
Edité par Springer, Berlin, Springer International Publishing, Springer, 2020
ISBN 10 : 3030610802 ISBN 13 : 9783030610807
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 69,16
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Ajouter au panierBuch. Etat : 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.
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EUR 101,02
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Edité par Springer Nature Singapore, Springer Nature Singapore Sep 2023, 2023
ISBN 10 : 9819948223 ISBN 13 : 9789819948222
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Edition originale
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Ajouter au panierBuch. Etat : Neu. Neuware -The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch.
Edité par Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10 : 9819948223 ISBN 13 : 9789819948222
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 95,65
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 110,06
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 115,78
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Edité par Springer-Nature New York Inc, 2023
ISBN 10 : 9819948223 ISBN 13 : 9789819948222
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 143,88
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Ajouter au panierHardcover. Etat : Brand New. 2nd edition. 242 pages. 9.25x6.10x9.21 inches. In Stock.
Vendeur : dsmbooks, Liverpool, Royaume-Uni
EUR 138,80
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Ajouter au panierHardcover. Etat : New. New. book.
Edité par Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10 : 3030610837 ISBN 13 : 9783030610838
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 57,59
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 int.
Edité par Springer International Publishing Dez 2021, 2021
ISBN 10 : 3030610837 ISBN 13 : 9783030610838
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 64,19
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Ajouter au panierTaschenbuch. Etat : 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. 152 pp. Englisch.
Edité par Berlin Springer International Publishing Springer Dez 2020, 2020
ISBN 10 : 3030610802 ISBN 13 : 9783030610807
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 64,19
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : 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.
Edité par Springer Nature Singapore, 2023
ISBN 10 : 9819948223 ISBN 13 : 9789819948222
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 79,72
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Explores advanced topics in deep learning encompassing transformer models, control theory, and graph neural networksPresents detailed mathematical descriptions and algorithms for generative pre-trained models, such as GPTsServes as a valuab.
Edité par Springer Nature Singapore Okt 2023, 2023
ISBN 10 : 9819948223 ISBN 13 : 9789819948222
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 90,94
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas. 244 pp. Englisch.