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Stochastic gradient descent is an optimization algorithm for finding the minimum or maximum of an objective function. In this Demonstration, stochastic gradient descent is used to learn the parameters (intercept and slope) of a simple regression problem. Using "contour plot", the likelihood function of the parameters is shown as a contour plot. The blue point gives the actual parameters while the red point shows the iterates of the stochastic gradient function. Selecting "regression" shows the data points, the actual regression line in green, and the iterative regression line in red as determined by the algorithm.

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      EUN,LOM,LRE4,work-cmr-id:262115,http://demonstrations.wolfram.com:http://demonstrations.wolfram.com/StochasticGradientDescent/,ilox,learning resource exchange,LRE metadata application profile,LRE

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