University:
Stanford University
Course:
CS229 | Machine Learning
Academic year:

2023
Views:

50

Pages:

1
Author:

anon

Gradient Descent in Linear Regression: An Understanding A popular optimization approach in machine learning and deep learning is gradient descent. To minimize a specific cost function, it is used to optimize a model's parameters. The Gradient Descent algorithm will be examined in the context of linear regression in this article, and we'll see how it aids in determining the best line of fit to the supplied data. Linear Regression: What Is It? A continuous variable can be predicted using the supervised machine learning technique of linear regression with one or more independent variables. In plain English, a line that best explains the relationship between the dependent and independent 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 the anticipated and actual values. A cost function, which is a mathematical representation of the model error, can be used to express this disparity. The Gradient Descent algorithm is used to minimize the cost function in order to determine the ideal model parameter values. The parameters are updated by the gradient descent algorithm in the direction of the cost function's sharpest descent. In other words, it goes in the direction of the cost function's minimum value. Until the cost function finds a global 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 different forms, each with pros and cons. Batch Gradient Descent is the linear regression type that is most frequently utilized. In Batch Gradient Descent, the algorithm examines all of the training instances at each update step rather than just a portion of the training data, as the name implies. How to use Gradient Descent in a program Gradient Descent is an algorithm that is simple to implement in code. Following are the steps: 1. Set arbitrary values for the parameters at initialization 2. 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.