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.

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.

 

 

• | Author: Alexander Horsch, Dilip K. Prasad, Ayush Somani
• | Publisher: Springer International Publishing AG
• | Publication Date: May 01, 2023
• | Number of Pages:
• | Language:
• | Binding: Hardback
• | ISBN-13: 9783031206382
• | ISBN-10: 303120638X