Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.

This project implements full-batch gradient descent (FBGD) for linear regression, comparing CPU serial and GPU implementations. The assignment demonstrates: assignment-5-linear-regression/ ├── ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...

Abstract: Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed ...

This project explores linear regression using both the least squares method and gradient descent. It implements the matrix form of linear regression and applies it to a real-world dataset. The ...

ABSTRACT: Monitoring of PM10 and PM2.5 concentrations frequently is essential for assessing air quality and informing pollution control strategies. This study examines the effect of height on PM2.5 ...

Efficient and exact traffic flow forecasting is critical for intelligent transportation systems. The number of model generations increases the computational complexity of deep neural network (DNN) ...