Interpretability in Deep Learning
Somani, Ayush.
Interpretability in Deep Learning [electronic resource] / by Ayush Somani, Alexander Horsch, Dilip K. Prasad. - 1st ed. 2023. - XX, 466 p. 176 illus., 172 illus. in color. online resource.
Chapter 1. Introduction -- Chapter 2. Neural networks for deep learning -- Chapter 3. Knowledge Encoding and Interpretation -- Chapter 4. Interpretation in Specific Deep Learning Architectures -- Chapter 5. Fuzzy 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. .
9783031206399
10.1007/978-3-031-20639-9 doi
Artificial intelligence.
Operations research.
Knowledge management.
Computer vision.
Artificial Intelligence.
Operations Research and Decision Theory.
Knowledge Management.
Computer Vision.
Q334-342 TA347.A78
006.3
Interpretability in Deep Learning [electronic resource] / by Ayush Somani, Alexander Horsch, Dilip K. Prasad. - 1st ed. 2023. - XX, 466 p. 176 illus., 172 illus. in color. online resource.
Chapter 1. Introduction -- Chapter 2. Neural networks for deep learning -- Chapter 3. Knowledge Encoding and Interpretation -- Chapter 4. Interpretation in Specific Deep Learning Architectures -- Chapter 5. Fuzzy 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. .
9783031206399
10.1007/978-3-031-20639-9 doi
Artificial intelligence.
Operations research.
Knowledge management.
Computer vision.
Artificial Intelligence.
Operations Research and Decision Theory.
Knowledge Management.
Computer Vision.
Q334-342 TA347.A78
006.3