How can you use rotation and translation to diversify deep learning training data?
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Deep learning models often require large and diverse datasets to learn effectively and avoid overfitting. However, collecting and labeling such datasets can be costly, time-consuming, and impractical. One way to overcome this challenge is to use data augmentation techniques, which create new and varied samples from existing data by applying transformations such as rotation and translation. In this article, you will learn how rotation and translation can diversify your deep learning training data and improve your model performance.