Working through the Multilayer Perceptron tutorial (http://deeplearning.net/tutorial/mlp.html) I noticed the omission of the bias vectors from the L1, L2 regularization calculations. The linked explanation of regularization indicates that the regularization term is calculated over the entire parameter vector (http://deeplearning.net/tutorial/gettingstarted.html#l1-l2-regularization).

It's easy enough to Google for an explanation of this, but it would be helpful if the omission of the bias vectors were explained directly in the MLP tutorial.