And let's say you've implement and regularize linear regression.

For logistic regression, we used to minimize the cost function j of theta that was minus 1 over m of this cost function and then plus this extra regularization term here, where this was a sum from j equals 1 through n, because we did not regularize the bias term theta zero.

like these, you know theta, i0 plus theta, i1, x1 plus, and so on, where I guess we could have a 2 there if this is the first hidden layer, and so the values with the 0 there at that corresponds to something that multiplies into an x0 or an a0 and so, this is kind of like a bias unit and by analogy to what we were doing for logistic progression, we won't sum over those terms in our regularization term because we don't want to regularize them and string the values 0.