Interpretability in Deep Learning - Dilip K. Prasad,Alexander Horsch,Ayush Somani
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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 i ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Dilip K. Prasad, Alexander Horsch, Ayush Somani |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Izlaides gads | 2023 |
| Vāka tips | Cietais vāks |
| EAN | 9783031206382 |