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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher | 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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Aggiungi al carrelloPaperback. Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Prima edizione
Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Federated and Transfer Learning | Roozbeh Razavi-Far (u. a.) | Taschenbuch | Adaptation, Learning, and Optimization | viii | Englisch | 2023 | Springer | EAN 9783031117503 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 380.
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
EUR 171,19
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Aggiungi al carrelloBuch. Condizione: 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.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 379 pages. 9.26x6.10x0.78 inches. In Stock.
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Aggiungi al carrelloHardcover. Condizione: Brand New. 379 pages. 9.25x6.10x0.88 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Da: AussieBookSeller, Truganina, VIC, Australia
Prima edizione
EUR 243,79
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 70,62
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: Books Puddle, New York, NY, U.S.A.
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