My last note shared a few ways to make gradient descent more efficient so that ML models can learn faster, how to check if gradient descent is working, and how the normal equation can sometimes be a good shortcut for training models. This note will focus in on just one new key idea: the Sigmoid function. The Sigmoid function (also known as the logistic function) is a function that outputs a value between 0 and 1 depending on its input. It's value to machine learning models comes from being able to take a set of inputs (parameters θ multiplied by their respective features x) and translate them into a number between 0 and 1, especially as a probability. Here, it's also good to know that the sigmoid function is often used as the function within the nodes of a neural network.