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Imagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. 2E:(f(x;) – t; )¤; 2.2 E:(f(x;) – t;) 3. E; (wx; + b – t;)x; 4. E: (wa; +b –- t;) 1 2 3 4

Imagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. 2E:(f(x;) – t; )¤; 2.2 E:(f(x;) – t;) 3. E; (wx; + b – t;)x; 4. E: (wa; +b –- t;) 1 2 3 4

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Imagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . n Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. E:(f(x;) – t;)x; 2.E(f(x;) – t;) n 3.- E;(wx; + b – t;)x; n 4. E: (wa; +b - t;) n 4
Transcribed Image Text:Imagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . n Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. E:(f(x;) – t;)x; 2.E(f(x;) – t;) n 3.- E;(wx; + b – t;)x; n 4. E: (wa; +b - t;) n 4
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