gradient when the gradient is big then you only need to move the parameters a little bit and you move your predictions by a lot so roughly in all of the ways we initialize the Hessian is something like going to zero with the width and the gradient is of one and so we have this sort of linear behavior in some region around the initialisation okay so any questions here before I move on to you saying the final state is an interpretive approximation an initial and that this linear approximation is good is this sort of an idealized model