Data science

The Gretel research team's favorite trends and takeaways from the NeurlPS 35th Annual Conference on Neural Information Processing Systems.

Look at how FastText word embeddings can help to quickly understand new datasets, and build more consistent labels for your own data.

A practical guide to creating differentially private, synthetic data with Python and TensorFlow.

Generate artificial records to balance biased datasets and improve overall model accuracy.

A brief exploration of model soup, the new ensembling technique that takes the average weights of multiple models to improve overall performance.

Use synthetic data to improve model accuracy for fraud, cyber security, or any classification task with an extremely limited minority class.

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

How to train a FastCUT GAN on public location data from a few cities to predict realistic e-bike locations across the world.

A step-by-step guide to creating high quality synthetic time-series datasets with Python.

We founded Gretel based on our beliefs that data shouldn’t be scary.