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Data Augmentation

Also known as data augmentation, it is a technique used in machine learning to increase the size of the dataset by applying transformations to existing data. This is achieved by applying various transformations to the existing data, such as rotations, scaling, translations, cropping, and other changes. The goal of data augmentation is to improve the model’s generalization and performance by providing more variety in the training data, which helps prevent overfitting.

Data augmentation is especially useful when there is a limited dataset available, as it allows for artificially expanding the dataset size, which can lead to better model performance and greater generalization ability.

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