bokomslag Interpretability in Deep Learning
Data & IT

Interpretability in Deep Learning

Ayush Somani Alexander Horsch Dilip K Prasad

Pocket

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  • 466 sidor
  • 2024
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.
  • Författare: Ayush Somani, Alexander Horsch, Dilip K Prasad
  • Format: Pocket/Paperback
  • ISBN: 9783031206412
  • Språk: Engelska
  • Antal sidor: 466
  • Utgivningsdatum: 2024-05-02
  • Förlag: Springer International Publishing AG