Consider the same house rent prediction problem where you are supposed to predict price of a house based on just its area. Suppose you have n samples with their respective areas, 2), ., x(n), their true house rents y(1), y(2),., y(n). Let's say, you train a linear regres- sor that predicts f(x) = 0 + 01x). The parameters 0, and 61 are scalars and are learned by minimizing mean-squared-error loss with L2-regularization through gradient descent with a learning rate a and the regularization strength constant A. Answer the following questions. ..... 1. Express the loss function(L) in terms of x), y(),n,0, 01, A. 2. Compute L 3. Compute ƏL 4. Write update rules for 0, and 01
Consider the same house rent prediction problem where you are supposed to predict price of a house based on just its area. Suppose you have n samples with their respective areas, 2), ., x(n), their true house rents y(1), y(2),., y(n). Let's say, you train a linear regres- sor that predicts f(x) = 0 + 01x). The parameters 0, and 61 are scalars and are learned by minimizing mean-squared-error loss with L2-regularization through gradient descent with a learning rate a and the regularization strength constant A. Answer the following questions. ..... 1. Express the loss function(L) in terms of x), y(),n,0, 01, A. 2. Compute L 3. Compute ƏL 4. Write update rules for 0, and 01
Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter11: Nonlinear Programming
Section11.1: Review Of Differential Calculus
Problem 7P
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