cerebro asked a year or two ago and they showed that this converges to the SVM solution in a very precise sense if you run gradient descent for a long long long time then you normalize because the norm blows up you get exactly the auto SVM solution after doing some normalization this is quite amazing if you haven't seen it before there's all these directions why the heck should gradient descent get the 1 of minimum how to norm that maximizes the l2 margin right I mean this seems maybe I should depend a loading rate how