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Interpretability in Deep Learning
  • Interpretability in Deep Learning
Interpretability in Deep Learning

Interpretability in Deep Learning

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Interpretability in Deep Learning

Somani, Ayush; Horsch, Alexander; Prasad, Dilip K.

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.

 

 

Details

Published by: Springer

Publication Date: 2023-05-01

Format: Hardcover

ISBN-13: 9783031206382

DOI: 10.1007/978-3-031-20639-9

Dimensions: 235cm x155cm

Pages: 466

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