Editore: Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
EUR 47,33
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.
EUR 82,89
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Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Da: Basi6 International, Irving, TX, U.S.A.
EUR 133,09
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Da: Books Puddle, New York, NY, U.S.A.
EUR 159,13
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Da: Majestic Books, Hounslow, Regno Unito
EUR 162,88
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Editore: Springer International Publishing, Springer International Publishing, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 160,49
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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.
Editore: Springer International Publishing, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 160,49
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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.
Editore: Springer International Publishing, Springer International Publishing Okt 2023, 2023
ISBN 10: 3031117506 ISBN 13: 9783031117503
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 160,49
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Editore: Springer International Publishing Okt 2022, 2022
ISBN 10: 3031117476 ISBN 13: 9783031117473
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 160,49
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Aggiungi al carrelloBuch. Condizione: 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.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 169,74
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 169,74
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EUR 162,15
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 170,59
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EUR 166,99
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EUR 169,72
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Da: California Books, Miami, FL, U.S.A.
EUR 181,66
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Da: California Books, Miami, FL, U.S.A.
EUR 181,66
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EUR 179,27
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EUR 185,43
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EUR 185,41
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EUR 184,62
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 195,09
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 187,07
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 188,69
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Editore: Springer International Publishing, 2023
ISBN 10: 3030913929 ISBN 13: 9783030913922
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 192,59
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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 addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications.
Editore: Springer International Publishing, 2022
ISBN 10: 3030913899 ISBN 13: 9783030913892
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 192,59
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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 addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications.
Editore: Springer International Publishing, Springer International Publishing Feb 2023, 2023
ISBN 10: 3030913929 ISBN 13: 9783030913922
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 192,59
Convertire valutaQuantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs¿ theoretical developments and their applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 160,95
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 213,91
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.