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Categorical Data: A Closer Look
Understand categorical data to help you create higher quality synthetic data
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Interpreting the Progress of CTGAN
It can be difficult to verify the progress that a GAN is making. What if we combined it with easily interpretable metrics and visualizations?
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How to evaluate synthetic data quality for your project — and avoid the biggest mistake we see
Evaluating synthetic data quality is critical. Avoid this common mistake and lead your project to success.
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User input to enhance synthetic data generation
ML models learn some rules out of the box, while other logic requires more work. Which is which? Read more to find out.
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