Regression: Let's say, we want to perform linear regression on a dataset containing m examples and n features. Our output is a linear funcion as follows: Ti = W₁xi,1 + W₂x₁,2 + .....+wnxin + b Now, if the error is E, then the gradient descent weight update rules should be as follows: W₁ = w₁ - for i {1,2, ..., n} dwi b=b-ASE For the following loss functions E, find and S. SE δω. 1. Mean Squared Error: 2. Sum of Squared Error: E = 1/²1(Yi - Ti )² Swi m E = ₁ (Yi - Yi)² 3. Mean Squared Logged Error: Sometimes, yi and yi can be too large. So, we use the following loss function. m E = (log yi - log y₁)²
Regression: Let's say, we want to perform linear regression on a dataset containing m examples and n features. Our output is a linear funcion as follows: Ti = W₁xi,1 + W₂x₁,2 + .....+wnxin + b Now, if the error is E, then the gradient descent weight update rules should be as follows: W₁ = w₁ - for i {1,2, ..., n} dwi b=b-ASE For the following loss functions E, find and S. SE δω. 1. Mean Squared Error: 2. Sum of Squared Error: E = 1/²1(Yi - Ti )² Swi m E = ₁ (Yi - Yi)² 3. Mean Squared Logged Error: Sometimes, yi and yi can be too large. So, we use the following loss function. m E = (log yi - log y₁)²
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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Please give a step-by-step solution.
Answer question 3.
Transcribed Image Text:Regression: Let's say, we want to perform linear regression on a dataset containing m examples
and n features. Our output is a linear funcion as follows:
Y₁ = W₁x₁,1 + w2xi,2+.......... + Wnxin + b
Now, if the error is E, then the gradient descent weight update rules should be as follows:
w₁ = w₁-X for i = {1,2,...,n}
b = b - Xow₂
SE
For the following loss functions E, find and
δων
1. Mean Squared Error:
2. Sum of Squared Error:
E = 1 (Yi-Yi)²
E = ₁ (Yi - yi)²
i=1
3. Mean Squared Logged Error: Sometimes, y₁ and yi can be too large. So, we use the following
loss function.
m
E = 1/21 (log yi – log yi)²
Li=1
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