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What is the role of regularization in deep learning?

The question is about deep learning

Answer

Regularization in deep learning is a technique used to prevent overfitting, where models perform well on training data but poorly on new, unseen data. Regularization methods, such as L1 and L2 regularization, add a penalty term to the loss function, discouraging overly complex models. Dropout is another regularization technique where random neurons are ignored during training, promoting redundancy and robustness. By controlling model complexity, regularization enhances generalization, leading to more reliable and accurate predictions.

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