Stochastic gradient descent is a variation of the gradient descent algorithm to train deep learning models. The basic idea is that instead of training the whole set of the data, a subset is utilized. Theoretically, one sample is good enough for training the network. But in practice, a fixed number of the input data or a batch is usually used. This approach results in faster training, as compared to the vanilla gradient descent.