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Don't have enough data to train your model? Fret not! Use the synthetic one!

 Synthetic data is artificially generated data that is not collected from real world events! It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.

 Synthetic data can be used for many applications:
- Privacy
- Removing Bias
- Balancing Datasets
- Augment Datasets

 Where to generate it from and how?

Open Source Project YData Synthetic: This repository contains material on GANs for synthetic data generation, especially regular tabular data and time-series. It consists a set of different GAN architectures developed using Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.

Link: https://github.com/ydataai/ydata-synthetic

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