Interpretability in Deep Learning

von Ayush Somani, Alexander Horsch, Dilip K. Prasad

466 Seiten, Hardcover

Kurzbeschreibung des Verlags

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.

 

 

• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI)
• ISBN: 9783031206382
• Sprache: Englisch
• Erscheinungsdatum: 01.05.2023
• Größe: 23.5 x 15.5 cm
• Verlag: Springer International Publishing