Interpretability in Deep Learning

By: Alexander Horsch, Dilip K. Prasad, Ayush Somani

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Hardcover | 1 May 2023 | Edition Number 2023

At a Glance

Hardcover
466 Pages

Dimensions(cm)
16.1 x 24.2 x 3.4

Hardcover

$394.75

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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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