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Interpretability in deep learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interpretability in deep learningby Ayush Somani, Alexander Horsch, Dilip K. Prasad.
Author:	Somani, Ayush.
other author:	Horsch, Alexander.
Published:	Cham :Springer International Publishing :2023.
Description:	xx, 466 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-3-031-20639-9
ISBN:	9783031206399$q(electronic bk.)

Interpretability in deep learning
Somani, Ayush.

Interpretability in deep learning[electronic resource] /by Ayush Somani, Alexander Horsch, Dilip K. Prasad. - Cham :Springer International Publishing :2023. - xx, 466 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Neural networks for deep learning -- Chapter 3. Knowledge Encoding and Interpretation -- Chapter 4. Interpretation in Specific Deep Learning Architectures -- Chapter 5. Fuzzy Deep Learning.

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.

ISBN: 9783031206399$q(electronic bk.)

Standard No.: 10.1007/978-3-031-20639-9doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Interpretability in deep learning
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505 0 $a Chapter 1. Introduction -- Chapter 2. Neural networks for deep learning -- Chapter 3. Knowledge Encoding and Interpretation -- Chapter 4. Interpretation in Specific Deep Learning Architectures -- Chapter 5. Fuzzy Deep Learning.
520 $a 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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