Verlag: Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Sprache: Englisch
Anbieter: Buchpark, Trebbin, Deutschland
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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.
Verlag: Cambridge University Press (edition 1), 2020
ISBN 10: 1107016908 ISBN 13: 9781107016903
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: As New. 1. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Verlag: Cambridge University Press, 2020
ISBN 10: 1107016908 ISBN 13: 9781107016903
Sprache: Englisch
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In den WarenkorbZustand: New. In.
Verlag: Cambridge University Press, 2020
ISBN 10: 1107016908 ISBN 13: 9781107016903
Sprache: Englisch
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
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In den WarenkorbZustand: New.
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In den WarenkorbHardcover. Zustand: Brand New. 379 pages. 9.00x6.00x0.75 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. 379 pages. 9.00x6.00x0.75 inches. In Stock.
Verlag: Cambridge University Press, 2020
ISBN 10: 1107016908 ISBN 13: 9781107016903
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 119,42
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Transfer learning deals with how machine learning and artificial intelligence systems can quickly adapt to new tasks and environments. This in-depth tutorial for students, researchers, and developers covers foundations, plus applications such as text mining, inference on social networks, recommendation, multimedia, and cyber-physical systems.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 160,25
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In den WarenkorbZustand: New. In.
Verlag: Springer International Publishing, Springer International Publishing Okt 2023, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 160,49
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch.
Verlag: Springer International Publishing Okt 2022, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
EUR 160,49
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In den WarenkorbBuch. Zustand: Neu. Neuware -This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch.
Verlag: Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 160,49
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Verlag: Springer International Publishing, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 160,49
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 166,34
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In den WarenkorbZustand: New. In.
EUR 237,33
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In den WarenkorbPaperback. Zustand: Brand New. 379 pages. 9.26x6.10x0.78 inches. In Stock.
EUR 239,39
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In den WarenkorbHardcover. Zustand: Brand New. 379 pages. 9.25x6.10x0.88 inches. In Stock.