What techniques do you use to prevent overfitting in deep learning models? Explain dropout, data augmentation, early stopping, and regularization, and discuss when to apply each.

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Overfitting occurs when models learn patterns specific to training data that do not generalize to new data. Deep networks with millions of parameters are especially prone to overfitting, making regularization techniques essential.