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We are given the following training examples (1.2, 3.2), (2.8, 8.5), (2,4.7), (0.9, 2.9), (5.1,11) Suppose the weights are wo = wi that corresponds to the half of the residual sum of squares; i.e., we have that = 1 initially. We want to minimize the cumulative loss Ls(h, c) ass,(t.e) = 1 RSSS(h, c) E(yi – h(x;))? Ls(h, c) = i=1 , Using n = 0.01, perform three iterations of full gradient descent, listing wo and wi (a) at each iteration. (b). What is the value that we predict at the following points x1 = 1.5 and x2 4.5?

We are given the following training examples (1.2, 3.2), (2.8, 8.5), (2,4.7), (0.9, 2.9), (5.1,11) Suppose the weights are wo = wi that corresponds to the half of the residual sum of squares; i.e., we have that = 1 initially. We want to minimize the cumulative loss Ls(h, c) ass,(t.e) = 1 RSSS(h, c) E(yi – h(x;))? Ls(h, c) = i=1 , Using n = 0.01, perform three iterations of full gradient descent, listing wo and wi (a) at each iteration. (b). What is the value that we predict at the following points x1 = 1.5 and x2 4.5?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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We are given the following training examples (1.2, 3.2), (2.8, 8.5), (2,4.7), (0.9, 2.9), (5.1, 11) Suppose the weights are wo = wi that corresponds to the half of the residual sum of squares; i.e., we have that = 1 initially. We want to minimize the cumulative loss Ls(h, c) m 1 RSSS(h, c) 1 Ls(h, c) = E(yi – h(x;))? i=1 , Using n = 0.01, perform three iterations of full gradient descent, listing wo and wi (a) at each iteration. (b). What is the value that we predict at the following points x1 = 1.5 and x2 4.5?
Transcribed Image Text:We are given the following training examples (1.2, 3.2), (2.8, 8.5), (2,4.7), (0.9, 2.9), (5.1, 11) Suppose the weights are wo = wi that corresponds to the half of the residual sum of squares; i.e., we have that = 1 initially. We want to minimize the cumulative loss Ls(h, c) m 1 RSSS(h, c) 1 Ls(h, c) = E(yi – h(x;))? i=1 , Using n = 0.01, perform three iterations of full gradient descent, listing wo and wi (a) at each iteration. (b). What is the value that we predict at the following points x1 = 1.5 and x2 4.5?
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