dataaugmentationia
Data augmentation is a technique used in machine learning and artificial intelligence to increase the size and diversity of a training dataset. This is achieved by applying various transformations to the existing data to create new, synthetic examples. The primary goal is to improve the generalization ability of machine learning models, making them more robust to variations in the input data and less prone to overfitting.
Common data augmentation techniques vary depending on the type of data. For images, these can include geometric
The benefits of data augmentation are significant. By exposing the model to a wider range of data