Interpretability in Deep Learning
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Beschreibung
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.
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.
Presents full coverage of interpretability in deep learning Explains the fundamental concepts of interpretability and the state of the art on the topic Includes fuzzy deep learning architectures
Autor*in
Ayush Somani
Themen in »Interpretability in Deep Learning«
Interpretability Deep Learning Interpretable Learning Neural Networks Explainable Artificial Intelligence Explainable Deep Learning Knowledge Encoding in Deep Learning Black-box Nature of Deep Learning