Imagine a regression model on a single feature, defined by the function f (x) = wx + b whereX, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² .nWork out the gradient with respect to b. Which is the correct answer? Read the four equationscarefully, so you notice all the differences.1. E:(f(x;) – t;)x;2.E(f(x;) – t;)n3.- E;(wx; + b – t;)x;n4. E: (wa; +b - t;)n4

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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