Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an iterative optimization algorithm used to minimize the cost function in machine learning and deep learning models. It is a variant of gradient descent algorithm that updates the model parameters using a small subset of training data, called a batch, at each iteration instead of the entire dataset. This makes SGD computationally efficient and scalable for large datasets. The batch size can be adjusted to balance between the speed of convergence and the accuracy of the model. SGD uses the gradient of the cost function with respect to the model parameters to update the parameters in the opposite direction of the gradient, which minimizes the cost function. The learning rate, which determines the step size of the parameter updates, can also be adjusted to improve the convergence of the algorithm.
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