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) = ;£;(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. 2 E(f(x;) – t:)xi 2 Σf() - t) 3. E: (wx; +b – t;)æ; 4. E:(wx; + b – t;) n 1 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) = ;£;(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. 2 E(f(x;) – t:)xi 2 Σf() - t) 3. E: (wx; +b – t;)æ; 4. E:(wx; + b – t;) n 1 3 4
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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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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