data augmentation techniques

Data augmentation techniques involve creating new, varied versions of existing data to improve a machine learning model’s performance. For example, with images, this might mean rotating, flipping, or adjusting brightness to simulate different conditions. In text, it could involve paraphrasing or adding slight modifications. These methods help models learn more robustly by exposing them to a wider range of scenarios, reducing overfitting and improving accuracy. Essentially, data augmentation acts like giving the model more diverse examples without acquiring new data, making it better at understanding and generalizing from what it already has.