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 by adding a penalty to the loss function. It helps models generalize better to new data by discouraging overly complex models. Common regularization methods include L1 and L2 regularization, which add constraints on the model's weights, and dropout, which randomly drops units during training to prevent co-adaptation. Regularization is crucial in deep learning, especially when dealing with limited data, as it helps maintain model performance and robustness.

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