L2 Regularization
L2 regularization is a technique used in machine learning and deep learning to prevent overfitting of the model. It adds a penalty term to the loss function of the model, which is proportional to the square of the magnitude of the weights in the model. This penalty term encourages the model to have smaller weights, which in turn reduces the complexity of the model and prevents it from fitting the noise in the data. L2 regularization is also known as weight decay, as it reduces the magnitude of the weights in the model. The strength of the regularization is controlled by a hyperparameter called the regularization parameter, which determines the trade-off between fitting the training data and reducing the complexity of the model.
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