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Data augmentation is a technique used in machine learning and deep learning to increase the size of the training set by creating modified versions of the original data. This is done by applying various transformations such as rotation, scaling, flipping, cropping, and adding noise to the original data. The purpose of data augmentation is to improve the performance of the model by reducing overfitting and increasing the generalization ability of the model. By creating more diverse data, the model can learn to recognize patterns and features that are invariant to these transformations, making it more robust to variations in the input data.
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