Sequential Gradient Descent

Sequential Gradient Descent is an optimization method used to improve models by updating parameters one at a time. Imagine adjusting a machine's settings step-by-step to reduce errors; you tweak one setting, see how it performs, then move to the next. This approach helps find the best values for each parameter individually, gradually lowering the overall error. It’s particularly useful when dealing with complex models, providing a systematic way to fine-tune parameters efficiently. Essentially, it’s an organized, step-by-step process to make a model learn better by continuously reducing mistakes.