Monthly Archives: February 2017

Linear Regression 5 – Other Practical Issues

In the previous articles we have discussed the basic concept of simple linear regression; how to measure the error of the regression model so that we can use the gradient descent method to find the global optimum of the regression problem; develop the multivariate linear regression model for real world problems; and how to choose… Read More »

Linear Regression 4 – Learning Rate and Initial Weights

Choosing Learning Rate We introduced an important parameter, the learning rate α, in Linear Regression 2 – Gradient Descent without discussing how to choose its value.  In fact, the choice of the learning rate affects the performance of the algorithm significantly.  It determines the convergence speed of the gradient descent algorithm, which is the number… Read More »