Interpretability in Deep Learning

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

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Paperback | 2 May 2023

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Paperback

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23.4 x 15.6 x 2.49

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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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