GradientDescentExample | Example demonstrating how gradient descent | Machine Learning library

GradientDescentExample is a Python library typically used in Artificial Intelligence, Machine Learning applications. GradientDescentExample has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However GradientDescentExample build file is not available. You can download it from GitHub.

This code demonstrates how a gradient descent search may be used to solve the linear regression problem of fitting a line to a set of points. In this problem, we wish to model a set of points using a line. The line model is defined by two parameters - the line's slope m, and y-intercept b. Gradient descent attemps to find the best values for these parameters, subject to an error function. The code contains a main function called run. This function defines a set of parameters used in the gradient descent algorithm including an initial guess of the line slope and y-intercept, the learning rate to use, and the number of iterations to run gradient descent for. Using these parameters a gradient descent search is executed on a sample data set of 100 ponts. Here is a visualization of the search running for 200 iterations using an initial guess of m = 0, b = 0, and a learning rate of 0.000005.