we initialize in network so it outputs an order one quantity that's random so this is like 1 because Y is order 1 and I basically pointed out that if you're Hessian divided by the square norm of the gradient is smaller than one then the second order term doesn't matter and your gradient dynamics track very closely for some constant amount of time the dynamics of a of gradient descent on a kernel machine this is very intuitive because it's essentially saying exactly that how much the Hessian changes is very small to the gradient and the