Data Augmentation

Data Augmentation is a technique used in Data Science and Artificial Intelligence to increase the size of a dataset by creating new data from the existing data. This technique is used to improve the performance of machine learning models by providing more data for training. Data Augmentation involves applying various transformations to the existing data, such as flipping, rotating, scaling, cropping, and adding noise. These transformations create new data points that are similar to the original data but have some variations. Data Augmentation is particularly useful when the size of the dataset is small, and the model is prone to overfitting. By increasing the size of the dataset, Data Augmentation helps to reduce overfitting and improve the generalization of the model.
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