you initialize it could depend on anything and this result is quite stable any learning rate that's stable it converges to the whole to SVM solution ok so then so what is then you might be thinking what is special about gradient descent let me write down gradient descent in a very suggestive way I'm just write it down so it's trying to maximize the correlation you know yeah because the logistic lost gradient is never zero so it's impossible to have a zero vanishing gradient even if you this is in contrast to least-squares I