Interpretability in Deep Learning
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 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.
年:
2023
出版商:
Springer Nature
語言:
english
頁數:
483
ISBN 10:
303120638X
ISBN 13:
9783031206382
文件:
EPUB, 97.02 MB
IPFS:
,
english, 2023
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