There are five main steps that are used to initialize and use the gradient descent algorithm :
* Initialize biases and weights for the network
* Send input data through the network (the input layer)
* Calculate the difference (the error) between expected and predicted values
* Change values in neurons to minimize the loss function
* Multiple iterations to determine the best weights for efficient working
