Data augmentation means making extra, slightly modified copies of your training data, so the model gets more practice without needing brand-new examples.
Imagine it like this: If you only have one photo of a cat, you could flip it, rotate it a little, brighten it, or zoom in — and now you have 5 different cat photos to train with, all from that one original!
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