Abdulrahman Kerim

Synthetic Data for Machine Learning (eBook, ePUB)

Revolutionize your approach to machine learning with this comprehensive conceptual guide

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 208
• Erscheinungstermin: 27. Oktober 2023
• Englisch
• ISBN-13: 9781803232607
• Artikelnr.: 69254901

Autorenporträt

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.