Interpretability in Deep Learning [electronic resource] /
Material type: TextPublisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023Description: XX, 466 p. 176 illus., 172 illus. in color. online resourceContent type:- text
- computer
- online resource
- 9783031206399
- 006.3 23
- Q334-342
- TA347.A78
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. .
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