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CS229 Lecture notes - Stanford Engineering Everywhere
see.stanford.eduCS229 Lecture notes Andrew Ng Supervised learning ... Note that the superscript “(i)” in the notation is simply an index into the training set, and has nothing to do with exponentiation. We will also use X denote the space of input values, and Y the …
CS229 Lecture Notes
cs229.stanford.edufor linear regression has only one global, and no other local, optima; thus gradient descent always converges (assuming the learning rate is not too large) to the global minimum. Indeed, J is a convex quadratic function. Here is an example of gradient descent as it is run to minimize a quadratic function. 5 10 15 20 25 30 35 40 45 50 5 10 15 20 ...