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Lecture 5: Stochastic Gradient Descent - Cornell University
Stochastic gradient descent (SGD).Basic idea: in gradient descent, just replace the full gradient (which is a sum) with a single gradient example. Initialize the parameters at some value w 0 2Rd, and decrease the value of the empirical risk iteratively by sampling a random index~i tuniformly from f1;:::;ng and then updating w t+1 = w t trf ~i t ...
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