Interpretability in Deep Learning by Ayush Somani, Alexander Horsch and Dilip K. Prasad [electronic resource] /
Material type: Computer filePublication details: Cham Springer International Publishing 2023Edition: 1st ed. 2023Description: XX, 466 p. 176 illus., 172 illus. in color. online resourceISBN:- 9783031206399
- 006.3
Item type | Home library | Call number | Status | Notes | Date due | Barcode | Item holds |
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e-Book | S. R. Ranganathan Learning Hub Online | Available | Platform:Springer | EB1885 |
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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