Search results with tag "Cs229"
CS 229, Fall 2018 ProblemSet#0: …
cs229.stanford.eduCS229 Problem Set #0 2 (a) Let z2Rn be an n-vector. Show that A= zzT is positive semide nite. (b) Let z2Rn be a non-zero n-vector. Let A= zzT.What is the null-space of A? What is the rank of A? (c) Let A2R n be positive semide nite and B2Rm …
CS229LectureNotes - CS229: Machine Learning
cs229.stanford.eduTo describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X → Y so that h(x) is a “good” predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this: Training set house.)
CS229 Supplemental Lecture notes Hoeffding’s inequality
cs229.stanford.eduCS229 Supplemental Lecture notes Hoeffding’s inequality John Duchi 1 Basic probability bounds A basic question in probability, statistics, and machine learning is the fol-lowing: given a random variable Z with expectation E[Z], how likely is Z to be close to its expectation? And more precisely, how close is it likely to be?
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 ...
CS229 Lecture notes - Stanford Engineering Everywhere
see.stanford.eduthe entire training set before taking a single step—a costly operation if m is large—stochastic gradient descent can start making progress right away, and continues to make progress with each example it looks at. Often, stochastic gradient descent gets θ …