The learning problem is modelled as a loss index minimization problem (f). ‘f’ denotes the function that evaluates a Neural Network's performance on a given dataset. The loss index is made up of two terms: an error component and a regularization term. While the error term analyses how well a Neural Network fits a dataset, the regularization term prevents overfitting by limiting the Neural Network's effective complexity. The Neural Network's adaptive variables – weights and biases – determine the loss function (f(w)). These variables can be bundled together into an unique n-dimensional weight vector (w).