In this recipe, we'll get our first taste of stochastic gradient descent. We'll use it for regression here.
Using SGD for regression
Getting ready
SGD is often an unsung hero in machine learning. Underneath many algorithms, there is SGD doing the work. It's popular due to its simplicity and speed—these are both very good things to have when dealing with a lot of data. The other nice thing about SGD is that while it's at the core of many machine learning algorithms computationally, it does so because it easily describes the process. At the end of the day, we apply some transformation on the data, and then we fit our data to the model with a loss function.
