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Interpretability in Deep Learning

by Ayush Somani, Alexander Horsch, Dilip K. Prasad

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About this book

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

Format
Hardcover
Pages
466
Publisher
Springer International Publishing
Language
EN
Edition
1st ed. 2023
ISBN-13
9783031206382
ISBN-10
303120638X

Categories

Computers, Artificial Intelligence, Business & Economics, Operations Research