Description

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.

Interpretability in Deep Learning

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Hardback by Ayush Somani , Alexander Horsch

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This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models,... Read more

    Publisher: Springer International Publishing AG
    Publication Date: 01/05/2023
    ISBN13: 9783031206382, 978-3031206382
    ISBN10: 303120638X

    Number of Pages: 466

    Non Fiction , Computing

    Description

    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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