Interpretability in Deep Learning [E-Book] / by Ayush Somani, Alexander Horsch, Dilip K. Prasad.
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 compute...
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Full text |
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Personal Name(s): | Somani, Ayush, author |
Horsch, Alexander, author / Prasad, Dilip K., author | |
Edition: |
1st edition 2023. |
Imprint: |
Cham :
Springer,
2023
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Physical Description: |
XX, 466 pages 176 illustrations, 172 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783031206399 |
DOI: |
10.1007/978-3-031-20639-9 |
Subject (LOC): |
- 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.