Machine Learning in Gradient Descent by Ricky Ho.
From the post:
In Machine Learning, gradient descent is a very popular learning mechanism that is based on a greedy, hill-climbing approach.
Gradient Descent
The basic idea of Gradient Descent is to use a feedback loop to adjust the model based on the error it observes (between its predicted output and the actual output). The adjustment (notice that there are multiple model parameters and therefore should be considered as a vector) is pointing to a direction where the error is decreasing in the steepest sense (hence the term “gradient”).
A general introduction to a machine learning technique you are going to see fairly often.