What is a common use of Stochastic Gradient Descent (SGD) in AI?

Stochastic Gradient Descent (SGD) is widely recognized as a central optimization technique used during the training of machine learning models, particularly in deep learning scenarios. Its primary function is to minimize the objective function, typically a loss function, by iteratively adjusting the model parameters based on the gradients calculated from a randomly selected subset of training data (or a single training example). This stochastic approach allows for faster updates compared to traditional gradient descent, which uses the entire dataset to compute gradients, thus speeding up the convergence of the optimization process.

The efficiency of SGD stems from its ability to handle large datasets, where computing gradients on the full dataset may be computationally expensive and time-consuming. By using random samples, SGD introduces variations in the gradient estimates, which can help escape local minima and potentially find better solutions. Moreover, through mini-batch SGD, the optimization process achieves a balance between the stability of full-batch gradient descent and the efficiency of the stochastic approach.

While other methods mentioned may pertain to different areas within AI, such as data normalization which is crucial for preprocessing, reinforcement learning which deals with agent-based learning, and graphical representation which focuses on visualizing data, they do not relate to the optimization of model parameters during training the way SGD does.