Learning with not Enough Data Part 3: Data Generation

1. 1Apply data augmentation techniques to existing training samples
2. 2Use generative models to produce new synthetic data
3. 3Evaluate the quality and diversity of generated data
4. 4Integrate generated data into training sets to improve model performance

Data scientists and machine learning engineers can benefit from this approach to improve model performance with limited data, and product managers can consider the impact of synthetic data on product development

💡 Synthetic data generation can help alleviate the problem of limited training data

🤖 Boost model performance with limited data using synthetic data generation!