torch.optim.SGD

torch.optim.SGD is a stochastic gradient descent optimizer in PyTorch that updates the parameters of a neural network to minimize the loss function. It works by computing the gradient of the loss function with respect to the parameters of the model and updating the parameters in the opposite direction of the gradient, multiplied by a learning rate.

SGD is stochastic because it randomly selects a subset of the training data (called a mini-batch) to compute the gradient at each iteration. This makes the optimization process faster and allows the algorithm to escape from local minima. However, it can also introduce noise into the gradient estimate, which can slow down convergence.

torch.optim.SGD allows the user to specify various hyperparameters such as the learning rate, momentum, weight decay, and nesterov momentum. These hyperparameters can significantly affect the performance of the optimizer and should be tuned carefully for each task.