Code: 41620668
Interpretability in Deep Learning
by Ayush Somani, Alexander Horsch, Dilip K. Prasad
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 ca ... more
- Language:
English - Binding: Hardback
- Number of pages: 463
Publisher: Springer, Berlin, 2023
- More about this
164.57 €
RRP: 178.34 €
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Book 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.
Book details
Book category Books in English Computing & information technology Computer science Artificial intelligence
164.57 €
- Full title: Interpretability in Deep Learning
- Author: Ayush Somani, Alexander Horsch, Dilip K. Prasad
- Language: English
- Binding: Hardback
- Number of pages: 463
- EAN: 9783031206382
- ID: 41620668
- Publisher: Springer, Berlin
- Weight: 948 g
- Dimensions: 235 × 155 mm
- Published: 2023
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