Subgradients

Summary

Hope you will enjoy this video. I know my voiceover is lacking some emotion but i will try my best to improve that for my next video. I recommend you watch in 1.25x or 1.5x to not waste time. This video is part of a full series on Parametric Regression in Machine Learning. Start from the beginning here: ... In today's lecture I'm going to talk about Gradient Descent (unconstrained optima of differentiable function) Pierre Schaus legt uit hoe de subgradiënt-algoritme wordt toegepast bij het oplossen van problemen met beperkingen. De presentatie behandelt strategieën voor het bijwerken van parameters en het waarborgen van haalbare oplossingen, evenals de complexiteit van het vinden van optimale waarden binnen dergelijke wiskundige modellen. So, in technical language you will say that g 1 and g 2 are the Neither the lasso nor the SVM objective function is differentiable, and we had to do some work for each to optimize with ... So we can define subgrading descent before we just replace the gradient by Okay so all we already covered all this and this is the jumping-off point just to give some