Verlag: Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Sprache: Englisch
Anbieter: WeBuyBooks, Rossendale, LANCS, Vereinigtes Königreich
EUR 10,46
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In den WarenkorbZustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 54,88
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In den WarenkorbZustand: New. In.
Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 397 | Sprache: Englisch | Produktart: Bücher.
Verlag: The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Sprache: Englisch
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 148,71
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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 154,95
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In den WarenkorbZustand: New. In.
Verlag: INSTITUTION OF ENGINEERING & T, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Sprache: Englisch
Anbieter: moluna, Greven, Deutschland
EUR 161,16
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In den WarenkorbZustand: New. KlappentextThis edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be appl.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 175,07
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In den WarenkorbZustand: New. In.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9811975566 ISBN 13: 9789811975561
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 178,02
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811975531 ISBN 13: 9789811975530
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 179,61
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
Verlag: Inst of Engineering & Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Sprache: Englisch
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 176,64
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 400 pages. 9.25x6.25x1.25 inches. In Stock.
Verlag: Institution Of Engineering & Technology Jun 2023, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 184,18
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbBuch. Zustand: Neu. Neuware - This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be applied to help individual patients.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 248,76
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 241 pages. 9.25x6.10x0.79 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 348,74
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In den WarenkorbHardcover. Zustand: Brand New. 300 pages. 9.45x6.69x0.79 inches. In Stock.