Gradient Descent, Data Science, Data Visualization, Deep Learning, Machine Learning.
depends on the specific problem and the trade-offs between convergence speed and computational efficiency. SGD is often preferred for large datasets or complex models due to its ability to update the model parameters more frequently and its scalability to parallel and distributed computing