Synthetic Data for Machine Learning: Revolutionize your approach to machine learning with this comprehensive conceptual guide

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