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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Data collection

The first step in the machine learning life cycle is data collection. It could be about collecting data from different public or commercial databases, storing user data back into your database or any data storage system you have, or even using commercial entities that take care of data collection and annotation for you. If you are relying on free resources, the main consideration for you could be the space the data will get in your local or cloud-based storage system and the time you need to spend to collect the data and analyze it in future steps. But for paid data, either provided in commercial resources or generated by data collection, generation, and annotation companies, you need to assess the value of the data for modeling before you decide to pay for it.

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

Table of Contents (26) Chapters

Data collection

Authors (1)

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Table of Contents (26) Chapters

Data collection

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