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Data Labeling in Machine Learning with Python

You're reading from   Data Labeling in Machine Learning with Python Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804610541
Length 398 pages
Edition 1st Edition
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Author (1):
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Vijaya Kumar Suda Vijaya Kumar Suda
Author Profile Icon Vijaya Kumar Suda
Vijaya Kumar Suda
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Labeling Tabular Data
2. Chapter 1: Exploring Data for Machine Learning FREE CHAPTER 3. Chapter 2: Labeling Data for Classification 4. Chapter 3: Labeling Data for Regression 5. Part 2: Labeling Image Data
6. Chapter 4: Exploring Image Data 7. Chapter 5: Labeling Image Data Using Rules 8. Chapter 6: Labeling Image Data Using Data Augmentation 9. Part 3: Labeling Text, Audio, and Video Data
10. Chapter 7: Labeling Text Data 11. Chapter 8: Exploring Video Data 12. Chapter 9: Labeling Video Data 13. Chapter 10: Exploring Audio Data 14. Chapter 11: Labeling Audio Data 15. Chapter 12: Hands-On Exploring Data Labeling Tools 16. Index 17. Other Books You May Enjoy

Labeling rules based on image visualization

Image classification is the process of categorizing an image into one or more classes based on its content. It is a challenging task due to the high variability and complexity of images. In recent years, machine learning techniques have been applied to image classification with great success. However, machine learning models require a large amount of labeled data to train effectively.

Image labeling using rules with Snorkel

Snorkel is an open source data platform that provides a way to generate large amounts of labeled data using weak supervision techniques. Weak supervision allows you to label data with noisy or incomplete sources of supervision, such as heuristics, rules, or patterns.

Snorkel primarily operates within the paradigm of weak supervision rather than traditional semi-supervised learning. Snorkel is a framework designed for weak supervision, where the labeling process may involve noisy, limited, or imprecise rules rather...

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