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Interpretability in Deep Learning. 2023 ed.
・ISBN 978-3-031-20638-2 hard EUR 159.99
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| 著者・編者 | Somani, Ayush / Horsch, Alexander / Prasad, Dilip K., |
|---|---|
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2023 |
| ページ数 | 466 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-10940＞ |
解説
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.