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Publicado por Springer International Publishing AG, Cham, 2023
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Añadir al carritoHardcover. Condición: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoBuch. Condición: 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.
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Añadir al carritoHardcover. Condición: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Publicado por Springer International Publishing Mai 2023, 2023
ISBN 10: 303120638X ISBN 13: 9783031206382
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 488 pp. Englisch.
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Añadir al carritoBuch. Condición: Neu. Interpretability in Deep Learning | Ayush Somani (u. a.) | Buch | xx | Englisch | 2023 | Springer | EAN 9783031206382 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Publicado por Springer, Springer Mai 2023, 2023
ISBN 10: 303120638X ISBN 13: 9783031206382
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 488 pp. Englisch.