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Sinopsis
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.
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De la contraportada
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.
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- EditorialSpringer
- Año de publicación2023
- ISBN 10 3031117506
- ISBN 13 9783031117503
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas380
- EditorRazavi-Far Roozbeh, Wang Boyu, Taylor Matthew E., Yang Qiang
- Contacto del fabricanteno disponible
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Federated and Transfer Learning
Publicado por
Springer International Publishing, 2023
ISBN 10: 3031117506
ISBN 13: 9783031117503
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Librería: Buchpark, Trebbin, Alemania
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Condición: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 42436269/1
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Federated and Transfer Learning
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Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031117506
ISBN 13: 9783031117503
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 w. Nº de ref. del artículo: 1094388812
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Federated and Transfer Learning
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Springer International Publishing Okt 2023, 2023
ISBN 10: 3031117506
ISBN 13: 9783031117503
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 380 pp. Englisch. Nº de ref. del artículo: 9783031117503
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Federated and Transfer Learning
Publicado por
Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031117506
ISBN 13: 9783031117503
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031117503
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Federated and Transfer Learning (Adaptation, Learning, and Optimization)
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Federated and Transfer Learning
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Federated and Transfer Learning (Adaptation, Learning, and Optimization)
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Federated and Transfer Learning
Publicado por
Springer International Publishing, Springer International Publishing Okt 2023, 2023
ISBN 10: 3031117506
ISBN 13: 9783031117503
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031117503
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Federated and Transfer Learning
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Federated and Transfer Learning
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