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.

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Spis treści

Synthetic Data for Machine Learning. Revolutionize your approach to machine learning with this comprehensive conceptual guide eBook -- spis treści

• 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