Interpretability in Deep Learning (1st ed. 2023)
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- Synopsis
- 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.
- Copyright:
- 2023
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783031206399
- Related ISBNs:
- 9783031206382
- Publisher:
- Springer International Publishing
- Date of Addition:
- 06/01/23
- Copyrighted By:
- The Editor
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Art and Architecture, Computers and Internet, Business and Finance
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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