Automated Generation of Synthetic Training Data for Machine Learning

Problem statement

Insufficient or biased training data can impact the performance of machine learning
models. Generating synthetic training data can address this issue, providing diverse and
balanced datasets for model training.

Abstract

This project aims to create a system for the automated generation of synthetic training
data for machine learning. Using techniques such as Generative Adversarial Networks
(GANs), the system will generate artificial data that closely resembles real-world
examples, improving the robustness and generalization of machine learning models.

Outcome

A tool capable of generating diverse and realistic synthetic training data, aiding in the
development of more robust and accurate machine learning models.