Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studies
Key Features
- Avoid common data issues by identifying and solving them using synthetic data-based solutions
- Master synthetic data generation approaches to prepare for the future of machine learning
- Enhance performance, reduce budget, and stand out from competitors using synthetic data
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Understand real data problems, limitations, drawbacks, and pitfalls
- Harness the potential of synthetic data for data-hungry ML models
- Discover state-of-the-art synthetic data generation approaches and solutions
- Uncover synthetic data potential by working on diverse case studies
- Understand synthetic data challenges and emerging research topics
- Apply synthetic data to your ML projects successfully
Who this book is for
If you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers.
Table of Contents
- Machine Learning and the Need for Data
- Annotating Real Data
- Privacy Issues in Real Data
- An Introduction to Synthetic Data
- Synthetic Data as a Solution
- Leveraging Simulators and Rendering Engines to Generate Synthetic Data
- Exploring Generative Adversarial Networks
- Video Games as a Source of Synthetic Data
- Exploring Diffusion Models for Synthetic Data
- Case Study 1 – Computer Vision
- Case Study 2 – Natural Language Processing
- Case Study 3 – Predictive Analytics
- Best Practices for Applying Synthetic Data
- Synthetic-to-Real Domain Adaptation
- Diversity Issues in Synthetic Data
- Photorealism in Computer Vision
- Conclusion
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.
