Synthetic Data for Machine Learning
eBook Details:
- Paperback: 208 pages
- Publisher: WOW! eBook (October 27, 2023)
- Language: English
- ISBN-10: 1803245409
- ISBN-13: 978-1803245409
eBook Description:
Synthetic Data for Machine Learning: Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studies
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.
- 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
By the end of this Synthetic Data for Machine Learning 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.