Gradient Descent: A Simple Explanation
The gradient descent algorithm is a powerful tool that is used in many different fields, including data science. In its simplest form, gradient descent is an optimization algorithm that can be used to find the minimum of a function. In other words, it can be used to find the values of variables that minimize a cost function.
There are many different variants of gradient descent, but they all share a common core: the ability to take small steps in the direction that minimizes the cost function. This process is repeated until a local minimum is found. The size of the steps taken is controlled by a parameter known as the learning rate.
Gradient descent works by iteratively moving in the direction that minimizes the cost function. To do this, it calculates the gradient of the cost function with respect to each of the variables. The gradient is simply a vector of partial derivatives. Once the gradient has been calculated, the algorithm takes a small step in that direction. This step is controlled by the learning rate. The learning rate determines how large or small the steps taken by the algorithm will be.
The algorithm then repeats this process until it converges on a local minimum. In some cases, it may not find the global minimum, but it will find a value of the variables that is close to the global minimum.
