Synthetic Tabular Data Generation Using Transformers

Synthetic data generation (SDG) is a data augmentation technique necessary for increasing the robustness of models by supplying training data. With advancements in pre-trained Transformers, data scientists across all industries are learning to use them to generate synthetic training data for downstream predictive tasks. In this course, you’ll explore the use of Transformers for synthetic tabular data generation. We will use credit card transactions data and the Megatron framework for the course, but this technique is broadly applicable to tabular data in general.

• Competency in the Python 3 programming language.
• Basic understanding of machine learning and deep learning concepts and pipelines.
• Experience building machine learning models with tabular data.
• Basic understanding of language modeling and transformers.

By participating in this course, you will:

• Learn how synthetic data can improve model performance.
• Learn to use Transformers for Synthetic Data Generation.
• Go through the end-to-end development workflow for generating synthetic data using Transformers, including data preprocessing, model pre-training, fine-tuning, inference, and evaluation.