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Interpretability in Deep Learning
Ayush Somani
(Author)
·
Alexander Horsch
(Author)
·
Dilip K. Prasad
(Author)
·
Springer
· Hardcover
Interpretability in Deep Learning - Somani, Ayush ; Horsch, Alexander ; Prasad, Dilip K.
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Synopsis "Interpretability in Deep Learning"
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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
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