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welcome back to pattern recognition so today we want to look a bit more into optimization and the topic today will be looking into the actual update direction so weamp;#39;ve seen the gradient descent methods in the previous video and today we want to have a couple of thoughts in how to pick the particular update direction [Applause] so these are these kind of steepest descent methods and the even the normalized ones we might want to consider what update direction we actually want to choose and what we actually want to get is the largest decrease in the linear approximation of f and technically we could also constrain this gradient direction by a unit ball of an lp norm so here we would then search for the update direction as the minimum over sum u that has a length of one ing to our norm and then the projection of the gradient onto this norm so we observe that if we do this kind of optimization for selecting the update direction then the steepest direction might not b
