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Active Machine Learning with Python

You're reading from   Active Machine Learning with Python Refine and elevate data quality over quantity with active learning

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835464946
Length 176 pages
Edition 1st Edition
Languages
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Author (1):
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Margaux Masson-Forsythe Margaux Masson-Forsythe
Author Profile Icon Margaux Masson-Forsythe
Margaux Masson-Forsythe
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Table of Contents (13) Chapters Close

Preface 1. Part 1: Fundamentals of Active Machine Learning
2. Chapter 1: Introducing Active Machine Learning FREE CHAPTER 3. Chapter 2: Designing Query Strategy Frameworks 4. Chapter 3: Managing the Human in the Loop 5. Part 2: Active Machine Learning in Practice
6. Chapter 4: Applying Active Learning to Computer Vision 7. Chapter 5: Leveraging Active Learning for Big Data 8. Part 3: Applying Active Machine Learning to Real-World Projects
9. Chapter 6: Evaluating and Enhancing Efficiency 10. Chapter 7: Utilizing Tools and Packages for Active ML 11. Index 12. Other Books You May Enjoy

Summary

In this chapter, we covered key techniques such as uncertainty sampling, query-by-committee, EMC, EER, and density weighting for designing effective active ML query strategies. Moving forward, in the next chapter, our focus will shift toward exploring strategies for managing the human in the loop. It is essential to optimize the interactions with the oracle labeler to ensure maximum efficiency in the active ML process. By understanding the intricacies of human interaction and leveraging this knowledge to streamline the labeling process, we can significantly enhance the efficiency and effectiveness of active ML algorithms.In the next chapter we will discuss how to manage the role of human labelers in active ML.

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