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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
Languages
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Author (1):
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Serg Masís Serg Masís
Author Profile Icon Serg Masís
Serg Masís
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

The mission

Self-checkout machines that allow customers to process their purchases were invented in 1984, but didn't start to appear in most supermarket chains until the turn of the century. However, despite the many advantages these machines generate for retailers and customers alike, they are far from perfect – they are prone to shoplifting, mechanical failures, lack of accessibility, and an inadequate customer service experience.

In the last decade, a lot of companies have been scrambling to fix these problems with deep learning. For instance, cameras can monitor body pose, product movement, and facial gestures. They can detect shoplifting events or even automatically lower the checkout to be more wheelchair accessible with trained deep learning models.

Another recent trend is that convenience store chains are experiencing a rapid growth phase in most developed countries. However, they struggle to keep up with demand and pay the low wages that allow them to be...

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