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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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Toc

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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “We define x_true and y_true.”

A block of code is set as follows:

y_true = np.array(small_dataset['label'])
x_true = np.array(small_dataset['text'])

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Anomaly detection is another domain where active learning proves to be highly effective.”

Tips or important notes

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