Lecture Note
University
Stanford UniversityCourse
CS229 | Machine LearningPages
1
Academic year
2023
anon
Views
30
Gradient Descent in Linear Regression: AnUnderstanding A popular optimization approach in machine learning and deep learning isgradient descent. To minimize a speciﬁc cost function, it is used to optimize amodel's parameters. The Gradient Descent algorithm will be examined in thecontext of linear regression in this article, and we'll see how it aids indetermining the best line of ﬁt to the supplied data. Linear Regression: What Is It? A continuous variable can be predicted using the supervised machine learningtechnique of linear regression with one or more independent variables. In plainEnglish, a line that best explains the relationship between the dependent andindependent variables is ﬁtted to the available data. What is the function of Gradient Descent in Linear Regression? The objective of linear regression is to reduce the discrepancy between theanticipated and actual values. A cost function, which is a mathematicalrepresentation of the model error, can be used to express this disparity. TheGradient Descent algorithm is used to minimize the cost function in order todetermine the ideal model parameter values. The parameters are updated by the gradient descent algorithm in thedirection of the cost function's sharpest descent. In other words, it goes in thedirection of the cost function's minimum value. Until the cost function ﬁnds aglobal minimum, which corresponds to the best line of ﬁt to the available data,this process is repeated numerous times. Gradient Descent Types The Gradient Descent method comes in many di erent forms, each with prosand cons. Batch Gradient Descent is the linear regression type that is mostfrequently utilized. In Batch Gradient Descent, the algorithm examines all of thetraining instances at each update step rather than just a portion of thetraining data, as the name implies. How to use Gradient Descent in a programGradient Descent is an algorithm that is simple to implement in code. Followingare the steps: 1. Set arbitrary values for the parameters at initialization2. Utilizing the present parameters, calculate the cost function.3. Adjust the settings in the direction of the cost function's steepest decline. 4. Repetition of steps 2 and 3 is necessary to get the cost function to a global minimum.
Gradient Descent in Linear Regression
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