Data & IT
Interpretability in Deep Learning
Ayush Somani • Alexander Horsch • Dilip K Prasad
Inbunden
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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 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.
- Format: Inbunden
- ISBN: 9783031206382
- Språk: Engelska
- Antal sidor: 466
- Utgivningsdatum: 2023-05-01
- Förlag: Springer International Publishing AG