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Synthetic Data for Machine Learning

You're reading from   Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803245409
Length 208 pages
Edition 1st Edition
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Author (1):
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Abdulrahman Kerim Abdulrahman Kerim
Author Profile Icon Abdulrahman Kerim
Abdulrahman Kerim
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Table of Contents (25) Chapters Close

Preface 1. Part 1:Real Data Issues, Limitations, and Challenges
2. Chapter 1: Machine Learning and the Need for Data FREE CHAPTER 3. Chapter 2: Annotating Real Data 4. Chapter 3: Privacy Issues in Real Data 5. Part 2:An Overview of Synthetic Data for Machine Learning
6. Chapter 4: An Introduction to Synthetic Data 7. Chapter 5: Synthetic Data as a Solution 8. Part 3:Synthetic Data Generation Approaches
9. Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data 10. Chapter 7: Exploring Generative Adversarial Networks 11. Chapter 8: Video Games as a Source of Synthetic Data 12. Chapter 9: Exploring Diffusion Models for Synthetic Data 13. Part 4:Case Studies and Best Practices
14. Chapter 10: Case Study 1 – Computer Vision 15. Chapter 11: Case Study 2 – Natural Language Processing 16. Chapter 12: Case Study 3 – Predictive Analytics 17. Chapter 13: Best Practices for Applying Synthetic Data 18. Part 5:Current Challenges and Future Perspectives
19. Chapter 14: Synthetic-to-Real Domain Adaptation 20. Chapter 15: Diversity Issues in Synthetic Data 21. Chapter 16: Photorealism in Computer Vision 22. Chapter 17: Conclusion 23. Index 24. Other Books You May Enjoy

Preface

Machine learning (ML) has made our lives far easier. We cannot imagine our world without ML-based products and services. ML models need to be trained on large-scale datasets to perform well. However, collecting and annotating real data is extremely expensive, error-prone, and subject to privacy issues, to name a few disadvantages. Synthetic data is a promising solution to real-data ML-based solutions.

Synthetic Data for Machine Learning is a unique book that will help you master synthetic data, designed to make your learning journey enjoyable. In this book, theory and good practice complement each other to provide leading-edge support!

The book helps you to overcome real data issues and improve your ML models’ performance. It provides an overview of the fundamentals of synthetic data generation and discusses the pros and cons of each approach. It reveals the secrets of synthetic data and the best practices to leverage it better.

By the end of this book, you will have mastered synthetic data and increased your chances of becoming a market leader. It will enable you to springboard into a more advanced, cheaper, and higher-quality data source, making you well prepared and ahead of your peers for the next generation of ML!

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