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Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2023
ISBN 10: 303120638X ISBN 13: 9783031206382
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In den WarenkorbHardcover. Zustand: Brand New. 486 pages. 9.25x6.10x1.22 inches. In Stock.
Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 303120638X ISBN 13: 9783031206382
Anbieter: moluna, Greven, Deutschland
Zustand: New. This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from applicat.
Taschenbuch. Zustand: Neu. Interpretability in Deep Learning | Ayush Somani (u. a.) | Taschenbuch | xx | Englisch | 2024 | Springer | EAN 9783031206412 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2024
ISBN 10: 303120641X ISBN 13: 9783031206412
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic.The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic.The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.