<human>:  What is the definition of stochastic gradient descent?
<bot>: Stochastic gradient descent (SGD) is an iterative method for optimizing an objective function and is based on minimizing a noisy estimate of the gradient of the objective function. It is a popular algorithm used in machine learning to find an approximate minimum value for a function. SGD works by incrementally updating parameters (such as weights in a neural network) in the direction that reduces error.