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Essential to the data science toolkit, synthetic data can help teams to deal with problems from small and imbalanced data, bias and privacy.
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Learn how to generate synthetic tabular data while leveraging the GAN-based model from the research to generate high-fidelity synthetic data in a data driven-approach.
You can find Tensorflow 2 implementations for some of the major architectures such as: Vanilla GAN, WGAN or WCGAN.
Sequential data can be found pretty much anywhere: from weather forecasted, exchange rates, to our mobility.
Learn how to capture dataset features distributions within each time-point with data-driven approaches such as: TimeGAN, CGAN or RCGAN.
