There are three variants of gradient descent as shown below:
Stochastic gradient descent: A single training example is used for the calculation of gradient and for updating parameters.
Batch gradient descent: Gradient is calculated for the entire dataset, and parameters are updated at every iteration.
Mini-batch gradient descent: Samples are broken down into smaller-sized batches and then worked on as in the case of stochastic gradient descent.