At Sention we were working with modest-sized datasets. We had to look at the dataset creatively and leverage synthetic data to build meaningful Machine learning models. In this talk, we will explore GANs and leveraging GANs to build synthetic data.
GANs leverage adversarial training mechanisms to produce artificial samples that closely resemble real data distributions. By harnessing the power of GANs, practitioners can efficiently generate synthetic data that retains the statistical properties of the original dataset while simultaneously preserving privacy, mitigating data scarcity issues, and facilitating robust model training across various domains.

Technical level: Technical practitioner

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