Learn how to use the stochastic gradient descent algorithm for classification and regression tasks.

Stochastic gradient descent (SGD) is a handy ML tool for finding the best answer to a problem. It’s especially useful when dealing with big piles of data or complicated models. Think of it like this: imagine you’re fixing a recipe and want the perfect taste. Instead of trying the whole recipe at once, you taste a bit, make a tiny adjustment, and repeat. That’s how SGD works. It makes little changes to the model’s settings step by step to find the best result, using just a small part of the data each time.

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