In this academic project, we implemented and compared betwen regression methods for linear, multi-linear and exponential problems. For the linear case, the implemented algorithms are : Newton method, ...

Because the linear ridge regression training algorithm presented in this article inverts a matrix, the technique doesn't scale to problems with huge amounts of training data. In such situations, it is ...

We will now implement Simple Linear Regression using PyTorch. Let us consider one of the simplest examples of linear regression, Experience vs Salary. We will train a regression model with a given set ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

This repository includes the C language implementation of some fundamental operations such as FFT, matrix inverse, matrix-vector multiplication, linear regression, 1D convolution and determinant of a ...

Learn the best practices for using linear regression to develop a recommendation system in AI. Apply them to different scenarios and improve your model performance.

Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results ...

A set of differential equations describing the dynamics of the transmission system and connected machines, including the voltage regulators and governors, is formed. The terms of these equations are ...

The second model we employ is Exponential Smoothing, a powerful tool for forecasting time series data. Both models are trained and tested using actual data obtained from the Jordanian Department of ...