l2 Regularization, also known as Ridge Regression, adds the squared values of the coefficients as a penalty term to the loss function. This approach is useful for minimizing large coefficients and is beneficial when dealing with multicollinearity. In the realm of nanotechnology, l2 regularization can enhance the stability and accuracy of models predicting the behavior of complex nanostructures.
