Interpretability in Deep Learning

[jockers]

pdf | 20.77 MB | English | Isbn:‎ B0BS5XKHW3 | Author: Ayush Somani | Year: 2023

Description:

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.

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