Debugging Machine Learning Models with Python

Table of Contents

1. Beyond Code Debugging
2. Machine Learning Life Cycle
3. Debugging toward Responsible AI
4. Detecting Performance and Efficiency Issues in Machine Learning Models
5. Improving the Performance of Machine Learning Models
6. Interpretability and Explainability in Machine Learning Modeling
7. Decreasing Bias and Achieving Fairness
8. Controlling Risks Using Test-Driven Development
9. Testing and Debugging for Production
10. Versioning and Reproducible Machine Learning Modeling
11. Avoiding and Detecting Data and Concept Drifts
12. Going Beyond ML Debugging with Deep Learning
13. Advanced Deep Learning Techniques
14. Introduction to Recent Advancements in Machine Learning
15. Correlation versus Causality
16. Security and Privacy in Machine Learning
17. Human-in-the-Loop Machine Learning