L2 regularization, also known as weight decay, is a technique used in machine learning to prevent overfitting by adding a penalty to the loss function proportional to the square of the magnitude of the coefficients. This method encourages the model to keep the coefficients small, which helps to create a simpler model that generalizes better to unseen data. By controlling the complexity of the model, L2 regularization plays a vital role in enhancing model performance and stability.
congrats on reading the definition of l2 regularization. now let's actually learn it.