Gradient descent is an optimization algorithm to find a point at which adifferentiable function is maximized (or minimized depending on whatyou wantto do). (Rigorously, speaking it is a local maximum or local minimum, but you canignore the distinction between global and local for the exam.) Gradient descentalgorithm is an iterative process. Here is how it works. First you choose 1 and aninitial point xo. You will find the next x, namely x1, by calculating xo – af' (xo)where f' is the first order derivative of f for which you want to find a maximumor minimum. In general, for any n, you get xn+1 = Xn – af'(xn). This iteration-stops when xn+1 and xn are close enough. -Suppose f (x) = x?. You want to find a point at which this function is minimizedusing the gradient descent algorithm. Your xo = -1 and 2 = 1. After how manysteps, you can find a point at which f is minimized?e

Name: Gradient descent is an optimization algorithm to find a point at whic
Uploaded: 2024-03-20T06:38:44.000Z
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Description: Gradient descent is an optimization algorithm to find a point at which adifferentiable function is maximized (or minimized depending on whatyou wantto do). (Rigorously, speaking it is a local maximum or local minimum, but you canignore the distincti

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Suppose f(x) = x². You want to find a point at which this function is minimized %3D using the gradient descent algorithm. Your xo = -1 and 1 = 1. After how many steps, you can find a point at which f is minimized?e