Explainable Deep Learning AI
eBook - ePub

Explainable Deep Learning AI

Methods and Challenges

  1. 346 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Explainable Deep Learning AI

Methods and Challenges

About this book

Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented. - Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI - Explores the latest developments in general XAI methods for Deep Learning - Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing - Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI

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Yes, you can access Explainable Deep Learning AI by Jenny Benois-Pineau,Romain Bourqui,Dragutin Petkovic,Georges Quenot in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title of Book
  2. Cover image
  3. Title page
  4. Table of Contents
  5. Copyright
  6. List of contributors
  7. Preface
  8. Chapter 1 Introduction
  9. Chapter 2 Explainable deep learning: concepts, methods, and new developments
  10. Chapter 3 Compact visualization of DNN classification performances for interpretation and improvement
  11. Chapter 4 Characterizing a scene recognition model by identifying the effect of input features via semantic-wise attribution
  12. Chapter 5 A feature understanding method for explanation of image classification by convolutional neural networks
  13. Chapter 6 Explainable deep learning for decrypting disease signatures in multiple sclerosis
  14. Chapter 7 Explanation of CNN image classifiers with hiding parts
  15. Chapter 8 Remove to improve?: Understanding CNN by pruning
  16. Chapter 9 Explaining CNN classifier using association rule mining methods on time-series
  17. Chapter 10 A methodology to compare XAI explanations on natural language processing
  18. Chapter 11 Improving malware detection with explainable machine learning
  19. Chapter 12 Explainability in medical image captioning
  20. Chapter 13 User tests & techniques for the post-hoc explanation of deep learning
  21. Chapter 14 Theoretical analysis of LIME
  22. Chapter 15 Conclusion
  23. Index