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Data Augmentation
Data augmentation is a set of techniques used to artificially increase the amount of data by creating modified copies of already existing data. For images, this might involve rotating, flipping, or cropping; for text, it could be paraphrasing or synonym replacement. It helps introduce more variability into the training set, making the model more robust.
In plain terms
It's like making many slightly different copies of a single photograph to have more practice material for an artist.
Why it matters
It improves a model's ability to generalize to new, unseen data, especially when original datasets are small.
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