Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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ISBN 10: 1108831745 ISBN 13: 9781108831741
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In den WarenkorbHardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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ISBN 10: 1108831745 ISBN 13: 9781108831741
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Verlag: Cambridge University Press 2021-09-23, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
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Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
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In den WarenkorbHardback. Zustand: New. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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EUR 67,38
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Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Brand New. 400 pages. 9.25x6.18x0.91 inches. In Stock.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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ISBN 10: 1108831745 ISBN 13: 9781108831741
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In den WarenkorbHardcover. Zustand: Brand New. 400 pages. 9.25x6.18x0.91 inches. In Stock.
Verlag: Cambridge University Press, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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In den WarenkorbZustand: New. This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and.
Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108831745 ISBN 13: 9781108831741
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 77,47
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In den WarenkorbHardback. Zustand: New. Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.