Training a Linear Regression Model in PyTorch
Linear regression is a simple yet powerful technique for predicting the values of variables based on other variables. It is often used for modeling relationships between two or more continuous variables, such as the relationship between income and age, or the relationship between weight and height. Likewise, linear regression can be used to predict continuous outcomes such as price or quantity demand, based on other variables that are known to influence these outcomes.
In order to train a linear regression model, we need to define a cost function and an optimizer. The cost function is used to measure how well our model fits the data, while the optimizer decides which direction to move in order to improve this fit.