What is data augmentation?
Data augmentation is a technique used to increase the diversity of your training dataset by applying various transformations.
Can you give me some examples of data augmentation techniques?
Sure! Common techniques include rotation, flipping, scaling, and adding noise to images.
How does data augmentation help in machine learning?
It helps improve model generalization by providing more varied data, reducing overfitting.
Are there any tools or libraries for data augmentation?
Yes, popular libraries include TensorFlow, Keras, and Albumentations, which offer built-in functions for augmentation.
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