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How can you prevent overfitting in neural networks?
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Answer / Ali Murtaza
Overfitting can be prevented by techniques such as regularization, early stopping, dropout, and data augmentation. Regularization adds a penalty term to the loss function to discourage large weights, while early stopping stops training when the validation error starts increasing. Dropout randomly disables some neurons during training to prevent co-adaptation among them, and data augmentation generates synthetic data to increase the size of the training set.
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