Which optimization algorithm is frequently used in training machine learning models?

Stochastic Gradient Descent (SGD) is a widely used optimization algorithm in training machine learning models, particularly when it comes to optimizing complex loss functions in large datasets. The core idea behind SGD is to approximate the gradient of the loss function (the function used to evaluate the performance of a model) by randomly selecting a subset (or mini-batch) of the total training data. This approach contrasts with traditional gradient descent, which computes the gradient using the entire dataset and can be computationally expensive and slow, especially for large datasets.

SGD has several advantages making it a popular choice. It typically requires less memory, allows for quicker updates to the model parameters, and can escape local minima more effectively due to its stochastic nature. The inherent randomness in SGD adds a bit of noise to the optimization process, which can help improve generalization and prevent overfitting.

In contrast, other options such as Linear Regression Algorithm, Random Forest Algorithm, and Support Vector Machine are not optimization algorithms themselves but rather models or machine learning techniques. They may utilize optimization algorithms like SGD as part of their underlying processes, but they do not serve the primary function of optimizing model parameters during training in the same fundamental way that SGD does. This distinction makes Stochastic Gradient Descent