Also known as: Mini batch Stochastic Gradient Descent
Mini-batch stochastic gradient descent is an optimization method that updates model parameters using gradients computed from small batches of training examples. It is widely used because it is more computationally efficient than full-batch gradient descent and usually more stable than updating from a single example at a time.
A gradient descent algorithm that uses mini-batches. In other words, mini-batch stochastic gradient descent estimates the gradient based on a small subset of the training data. Regular stochastic gradient descent uses a mini-batch of size 1.