Synthetic Data for Machine Learning: Revolutionize your approach to machine learning with this comprehensive conceptual guide

by Abdulrahman Kerim

Overview

The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges.
This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data.
By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.

Product Details

ISBN-13:	9781803232607
Publisher:	Packt Publishing
Publication date:	10/27/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	208
File size:	10 MB

About the Author

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.

Table of Contents

Table of Contents

1. Machine Learning and the Need for Data
2. Annotating Real Data
3. Privacy Issues in Real Data
4. An Introduction to Synthetic Data
5. Synthetic Data as a Solution
6. Leveraging Simulators and Rendering Engines to Generate Synthetic Data
7. Exploring Generative Adversarial Networks
8. Video Games as a Source of Synthetic Data
9. Exploring Diffusion Models for Synthetic Data
10. Case Study 1 – Computer Vision
11. Case Study 2 – Natural Language Processing
12. Case Study 3 – Predictive Analytics
13. Best Practices for Applying Synthetic Data
14. Synthetic-to-Real Domain Adaptation
15. Diversity Issues in Synthetic Data
16. Photorealism in Computer Vision
17. Conclusion