Why We Need Gradient Descent In the previous article, Linear Regression 1 – Simple Linear Regression and Cost Function, we introduced the concept of simple linear regression, which is basically to find a regression line model $$M_w(x) = w_0 + w_1x_1$$ so that the prediction \(M_w(x)\) is as close to the \(y\) of our training data \((x,y)\) as possible. To find the best fit regression line, we are actually finding the optimal combination of the weight parameters \(w_0\) and \(w_1\) and trying to minimize the errors between the predictions and the actual values of target feature \(y\).

How to represent a model in simple linear regression, and how to calculate the cost function to determine the fitness of the model.

Powered by the Academic theme for Hugo.