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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

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803245409

Length 208 pages

Edition 1st Edition

Concepts

Data Science

Author (1):

Abdulrahman Kerim

View More author details

Table of Contents (25) Chapters

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

Why subscribe?

24. Other Books You May Enjoy

Real-world case studies

We discussed and presented the effectiveness of utilizing synthetic data in real-life scenarios, by providing diverse real-world examples of the successful utilization of synthetic data in computer vision (Chapter 10), natural language processing (Chapter 11), and predictive analytics (Chapter 12). Then, we learned about some well-known domain-specific issues that limit the usability of synthetic data and uncovered some best practices to effectively utilize synthetic data in practice (Chapter 13).

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Authors (1)

Abdulrahman Kerim

Abdulrahman Kerim

Abdulrahman Kerim is a full-time lecturer at UCA and an active researcher at the School of Computing and Communications at Lancaster University, UK. Kerim has an MSc in Computer Engineering with a focus on developing a simulator for computer vision problems. In 2020, Kerim commenced his PhD to investigate synthetic data advantages and potentials. His research on developing novel synthetic-aware computer vision models has been recognized internationally. He published several papers on the usability of synthetic data at top-tier conferences and journals, such as BMVC and IMAVIS. He is currently working with researchers from Google and Microsoft to overcome real-data issues specifically for video stabilization and semantic segmentation tasks.

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