Book Image

Synthetic Data for Machine Learning

By : Abdulrahman Kerim
Book Image

Synthetic Data for Machine Learning

By: Abdulrahman Kerim

Overview of this book

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.
Table of Contents (25 chapters)
1
Part 1:Real Data Issues, Limitations, and Challenges
5
Part 2:An Overview of Synthetic Data for Machine Learning
8
Part 3:Synthetic Data Generation Approaches
13
Part 4:Case Studies and Best Practices
18
Part 5:Current Challenges and Future Perspectives

To get the most out of this book

You will need a version of PyCharm installed on your computer – the latest version, if possible. All code examples have been tested using Python 3.8 and PyCharm 2023.1 (Professional Edition) on Ubuntu 20.04.2 LTS. However, they should work with future version releases, too.

Software/hardware covered in the book

Operating system requirements

Python 3.8+

Windows, macOS, or Linux

PyCharm 2023.1

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.