augment-data

Data augmentation is a strategy used in machine learning to enhance the diversity of training data without the need for collecting new samples. By applying transformations such as rotation, scaling, flipping, or adding noise to existing data points, the model can learn to generalize better to new, unseen examples. This technique is particularly popular in image processing and natural language processing to improve model robustness.