Machine Learning

How we generated high-quality synthetic time-series data for one of the largest financial institutions in the world.

A look at how using Gretel’s Privacy Filters to immunize synthetic datasets against adversarial attacks can impact machine learning accuracy.

Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.

How to use Weights & Biases’ ML hyperparameter sweeps tool to optimize the accuracy of your synthetic data.

An evaluation of the effect of sampling procedures on the quality of synthetic tabular data using Gretel.ai's Synthetic Quality Score (SQS).

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.

Explore how to create a batch interface with the latest version of Gretel Synthetics on Google Colaboratory.

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

We explore the popular open-source package Optuna to demonstrate how you can optimize your model hyperparameters and build the best synthetic model possible.