Interpretability in Deep Learning

By Ayush Somani, Dilip K. Prasad, Alexander Horsch

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: Hardcover
• Language: English
• ISBN: 303120638X
• ISBN13: 9783031206382
• Release Date: May 2023
• Publisher: Springer
• Length: 466 Pages
• Weight: 2.00 lbs.
• Dimensions: 1.1" x 6.2" x 9.4"