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.
CITATION STYLE
Somani, A., Horsch, A., & Prasad, D. K. (2023). Interpretability in Deep Learning. Interpretability in Deep Learning (pp. 1–466). Springer International Publishing. https://doi.org/10.1007/978-3-031-20639-9
Mendeley helps you to discover research relevant for your work.