Overcome real data issues, improve your machine learning models' performance, and lead your field. Master cutting-edge synthetic data generation techniques. Unlock the secrets, grasp the best practices, and foresee the future.
Key Features
- Avoid common data issues by identifying and solving them using synthetic data-based solutions
- Prepare for the future of machine learning by mastering synthetic data generation approaches
- Improve performance, reduce budget, and stand out from competitors by using synthetic data
Book Description
Machine learning has made our lives far easier. We cannot imagine our world without machine learning-based products and services. Machine learning models need to be trained on large scale datasets to perform well. However, collecting and annotating real data is extremely expensive, error-prone, and subject to privacy issues to name a few. Synthetic data is a promising solution to real-data machine learning-based solutions.
Synthetic Data for Machine Learning is a unique book to help you master synthetic data, designed to make your learning journey enjoyable. In this book, theory and good practice complement each other to provide leading-edge support!
The book helps you to overcome real data issues and improve your machine learning models' performance. It provides an overview of the fundamentals of synthetic data generation and discusses the pros and cons of each approach. It reveals the secrets of synthetic data and the best practices to leverage it better.
By the end of this book, you will master synthetic data and increase your chances of becoming a market leader. It will enable you to springboard into a more advanced, cheaper, and higher-quality data source, making you well-prepared and ahead of your peers for the next generation of machine learning!
What you will learn
- Understand real data problems, limitations, drawbacks, and pitfalls
- Use synthetic data as a solution for data-hungry ML models
- Discover state-of-the-art synthetic data generation approaches
- Uncover synthetic data potential by looking at diverse case studies
- Understand synthetic data challenges and hot research topics
- Successfully apply synthetic data to your machine learning project
Who This Book Is For
If you are a Machine Learning (ML) practitioner or researcher who wants to overcome data problems in ML, this book is written especially for you! It assumes you have basic knowledge of ML and Python programming (not more!). The book was carefully designed to give you the foremost guidance to master synthetic data for ML. It builds your knowledge gradually from synthetic data concepts and algorithms to applications, study cases, and best practices. The book is one of the pioneer works on the subject providing leading-edge support for ML engineers, researcher, companies, and decision makers.
Table of Contents
- Machine learning and the need for data
- Annotating real data: the bottleneck
- Privacy issues with real data
- An Introduction to synthetic data
- Synthetic data as a solution
- Leveraging simulators and rendering engines to generate synthetic data
- Generative Adversarial Networks (GANs)
- Video games as a source of synthetic data
- Diffusion Models
- Case study 1: Computer vision
- Case study 2: NLP
- Case study 3: Predictive analytics
- Good practices for applying synthetic data
- Synthetic-to-real domain adaptation
- Diversity issues
- Photo-realism
- Conclusion
