TY - BOOK

T1 - Synthetic Data for Machine Learning

AU - Kerim, Abdulrahman

PY - 2023/10/27

Y1 - 2023/10/27

N2 - Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studiesKey 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 eBookBook DescriptionThe 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 successfullyWho this book is forIf 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 Contents1. Machine Learning and the Need for Data2. Annotating Real Data3. Privacy Issues in Real Data4. An Introduction to Synthetic Data5. Synthetic Data as a Solution6. Leveraging Simulators and Rendering Engines to Generate Synthetic Data7. Exploring Generative Adversarial Networks8. Video Games as a Source of Synthetic Data9. Exploring Diffusion Models for Synthetic Data10. Case Study 1 – Computer Vision11. Case Study 2 – Natural Language Processing12. Case Study 3 – Predictive Analytics13. Best Practices for Applying Synthetic Data14. Synthetic-to-Real Domain Adaptation15. Diversity Issues in Synthetic Data16. Photorealism in Computer Vision17. Conclusion

AB - Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studiesKey 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 eBookBook DescriptionThe 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 successfullyWho this book is forIf 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 Contents1. Machine Learning and the Need for Data2. Annotating Real Data3. Privacy Issues in Real Data4. An Introduction to Synthetic Data5. Synthetic Data as a Solution6. Leveraging Simulators and Rendering Engines to Generate Synthetic Data7. Exploring Generative Adversarial Networks8. Video Games as a Source of Synthetic Data9. Exploring Diffusion Models for Synthetic Data10. Case Study 1 – Computer Vision11. Case Study 2 – Natural Language Processing12. Case Study 3 – Predictive Analytics13. Best Practices for Applying Synthetic Data14. Synthetic-to-Real Domain Adaptation15. Diversity Issues in Synthetic Data16. Photorealism in Computer Vision17. Conclusion

M3 - Book

SN - 9781803245409

VL - 1

BT - Synthetic Data for Machine Learning

PB - Packt Publishing

ER -