dataaugmantatie
Data augmentation is a technique used in machine learning and deep learning to artificially increase the size and diversity of a training dataset. This is achieved by applying various transformations to existing data samples, creating new, modified versions that are still representative of the original data's characteristics. The primary goal of data augmentation is to improve the robustness and generalization ability of machine learning models, preventing them from overfitting to the specific training examples and performing better on unseen data.
Common data augmentation techniques vary depending on the type of data. For images, transformations include rotations,
By generating more varied training examples, data augmentation helps models learn more invariant features. For instance,