Tutorials
Welcome! We’re excited to share our repository of tutorials with you, which will help you explore and validate the benefits of synthetic data. Simply clone the repository to your own environment and run it locally via Jupyter Lab, or make it even easier and run each tutorial directly on Google’s cloud resources via Colab. Let’s get started!
| Tutorial | Colab Link | GitHub Link |
|---|---|---|
Get started with the SDK |  |  |
Validate synthetic data via Train-Synthetic-Test-Real | | |
Explore the size vs. accuracy trade-off for synthetic data | | |
Differentially private synthetic data | | |
Rebalance synthetic datasets for data augmentation | | |
Conditionally generate synthetic (geo) data | | |
Explain AI with synthetic data | | |
Generate fair synthetic data | | |
Generate synthetic text via a fast LSTM model trained from scratch | | |
Generate synthetic text via a pre-trained Large Language Model | | |
Perform multi-table synthesis | | |
Analyse star-schema correlations | | |
Develop a fake or real discriminator with Synthetic Data | | |
Close gaps in your data with Smart Imputation | | |
Calculate accuracy and privacy metrics for Quality Assurance | | |