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What is data augmentation in AI?

Data augmentation is a technique used in machine learning to increase the size and diversity of the training data. This is done by creating modified versions of the existing data, such as by rotating or scaling images, or by adding noise to audio data.

Why is data augmentation used?

Data augmentation is used to improve the performance of machine learning models. By providing more and varied training examples, it helps the model learn to generalize better to unseen data. This is particularly useful when the available training data is limited.

Data augmentation can also help reduce overfitting, as it encourages the model to learn invariant features.

What are some common data augmentation techniques?

Common data augmentation techniques include geometric transformations (such as rotation, scaling, and flipping), color transformations (such as brightness and contrast adjustments), and adding noise. The choice of data augmentation techniques depends on the nature of the data and the task at hand.

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