You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781800208582

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Author (1):

Ali Madani

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Table of Contents (26) Chapters

Preface

1. Part 1:Debugging for Machine Learning Modeling

2. Chapter 1: Beyond Code Debugging FREE CHAPTER

3. Chapter 2: Machine Learning Life Cycle

4. Chapter 3: Debugging toward Responsible AI

5. Part 2:Improving Machine Learning Models

6. Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models

7. Chapter 5: Improving the Performance of Machine Learning Models

8. Chapter 6: Interpretability and Explainability in Machine Learning Modeling

9. Chapter 7: Decreasing Bias and Achieving Fairness

10. Part 3:Low-Bug Machine Learning Development and Deployment

11. Chapter 8: Controlling Risks Using Test-Driven Development

12. Chapter 9: Testing and Debugging for Production

13. Chapter 10: Versioning and Reproducible Machine Learning Modeling

14. Chapter 11: Avoiding and Detecting Data and Concept Drifts

15. Part 4:Deep Learning Modeling

16. Chapter 12: Going Beyond ML Debugging with Deep Learning

17. Chapter 13: Advanced Deep Learning Techniques

18. Chapter 14: Introduction to Recent Advancements in Machine Learning

19. Part 5:Advanced Topics in Model Debugging

20. Chapter 15: Correlation versus Causality

21. Chapter 16: Security and Privacy in Machine Learning

22. Chapter 17: Human-in-the-Loop Machine Learning

23. Assessments

24. Index

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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A/B testing 41

Adaptive synthetic (ADASYN) 98

Advanced Encryption Standard (AES) 280

implementing, in Python 280, 281

adversarial attacks 49

algorithmic bias 48

alibi_detect

practicing, for drift detection 201, 202

Anchor explanations 125

Ansible 179

reference link 179

Artificial Intelligence (AI) 3

Artificial Intelligence (AI) Act 54

artificial neural networks (ANNs) 210-212

optimization algorithms 212, 213

assertions 16

AttributeError 9

automation 4

autoregressive models 259

Bayesian networks 272, 273

causal inference, with bnlearn 275-277

Bayesian search 96

behavior-driven development (BDD) testing 180

bias

in data generation and collection 147-150

in model training and testing 151

in production 151

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Table of Contents (26) Chapters

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Table of Contents (26) Chapters

Index

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