Gradient descent is an iterative approach for error correction in any learning model. For neural networks during backpropagation, the process of iterating the update of weights and biases with the error times derivative of the activation function is the gradient descent approach. The steepest descent step size is replaced by a similar size from the previous step. Gradient is basically defined as the slope of the curve and is the derivative of the activation function:

The objective of deriving gradient descent at each step is to find the global cost minimum, where the error is the lowest. And this is where the model has a good fit for the data and predictions are more accurate.

Gradient descent can be performed either for the full batch or stochastic. In full batch gradient descent, the gradient is computed for the full training dataset, whereas Stochastic Gradient Descent (SGD) takes a single sample and performs gradient calculation. It can also take mini-batches and perform the calculations. One advantage of SGD is faster computation of gradients.