Regularization in machine learning and deep learning is a technique used to prevent overfitting by adding a penalty term to the loss function.
It helps control the complexity of the model, discouraging excessive reliance on specific features.
By imposing constraints on the model parameters, regularization promotes generalization, enhancing the model's ability to perform well on new, unseen data.
Techniques like L1 and L2 regularization are commonly employed to strike a balance between fitting the training data and avoiding unnecessary complexity.
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