Data Augmentation is a technique used to expand the size and variety of a dataset by producing modified versions of the existing data. This can be achieved by applying minor changes like flipping, resizing, or altering brightness levels to images. Though it may seem like a small addition, data augmentation can have a significant impact on the performance of a machine-learning model. Providing the model with a more diverse training set helps prevent overfitting and ensures that it can handle a wider range of inputs.
