K and what is happy there we don't really know but I think that's probably likely to correspond to closer to practice than the other end point because things like large learning rate finite width will cause you to sort of stay infinitely close yeah okay so the final thing I want to talk about is how does architecture matter so I've told you kind of that gradient descent asymptotically the bias it gets you is an l2 regularization on all of the parameters but that's sort of uninteresting why should you ever care