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Hands-On Explainable AI (XAI) with Python

You're reading from Hands-On Explainable AI (XAI) with Python Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781800208131

Length 454 pages

Edition 1st Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

View More author details

Table of Contents (16) Chapters

Preface

1. Explaining Artificial Intelligence with Python

2. White Box XAI for AI Bias and Ethics FREE CHAPTER

3. Explaining Machine Learning with Facets

4. Microsoft Azure Machine Learning Model Interpretability with SHAP

5. Building an Explainable AI Solution from Scratch

6. AI Fairness with Google's What-If Tool (WIT)

7. A Python Client for Explainable AI Chatbots

8. Local Interpretable Model-Agnostic Explanations (LIME)

9. The Counterfactual Explanations Method

10. Contrastive XAI

11. Anchors XAI

12. Cognitive XAI

13. Answers to the Questions

14. Other Books You May Enjoy

15. Index

References

The original repository for the programs that were used in this chapter can be found on GitHub: Scott Lundberg, slundberg, Microsoft Research, Seattle, WA, https://github.com/slundberg/shap

The algorithms and visualizations come from:

Su-In Lee's lab at the University of Washington
Microsoft Research

Reference for linear models: https://scikit-learn.org/stable/modules/linear_model.html

The rest of the chapter is locked

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Authors (1)

Denis Rothman

Denis Rothman

Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author

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