| Description |
1 online resource (390 pages) : illustrations (black and white, and color) |
| Contents |
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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Description based on online resource; title from digital title page (viewed on June 06, 2023) |
| Subject |
Deep learning (Machine learning)
|
|
Deep learning (Machine learning)
|
| Form |
Electronic book
|
| Author |
Horsch, Alexander, author
|
|
Prasad, Dilip K. author
|
| ISBN |
9783031206399 |
|
3031206398 |
|
