| 书目名称 | Interpretability in Deep Learning |
| 编辑 | Ayush Somani,Alexander Horsch,Dilip K. Prasad |
| 视频video | http://file.papertrans.cn/473/472699/472699.mp4 |
| 概述 | Presents full coverage of interpretability in deep learning.Explains the fundamental concepts of interpretability and the state of the art on the topic.Includes fuzzy deep learning architectures |
| 图书封面 |  |
| 描述 | .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.. . . . . |
| 出版日期 | Book 2023 |
| 关键词 | Interpretability; Deep Learning; Interpretable Learning; Neural Networks; Explainable Artificial Intelli |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-20639-9 |
| isbn_softcover | 978-3-031-20641-2 |
| isbn_ebook | 978-3-031-20639-9 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手机版|小黑屋|
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