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Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems: 1100 (Studies in Computational Intelligence) - Softcover
Inhaltsangabe
The book provides a timely coverage of the paradigm of knowledge distillation—an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher–student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.
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The book provides a timely coverage of the paradigm of knowledge distillation--an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher-student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.
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- VerlagSpringer
- Erscheinungsdatum2024
- ISBN 10 3031320972
- ISBN 13 9783031320972
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten244
- HerausgeberPedrycz Witold, Chen Shyi-Ming
- Kontakt zum HerstellerNicht verfügbar
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Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems
ISBN 10: 3031320972
ISBN 13: 9783031320972
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Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems
ISBN 10: 3031320972
ISBN 13: 9783031320972
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Taschenbuch. Zustand: Neu. Neuware -The book provides a timely coverage of the paradigm of knowledge distillation¿an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher¿student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch. Artikel-Nr. 9783031320972
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Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems
Verlag:
Springer International Publishing, 2024
ISBN 10: 3031320972
ISBN 13: 9783031320972
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Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book provides a timely coverage of the paradigm of knowledge distillation-an efficient way ofmodel compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher-student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms. Artikel-Nr. 9783031320972
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