What variant of gradient descent updates model parameters using only a single training instance at each step?
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Stochastic Gradient Descent
SGD introduces noise into the optimization path, which can help escape local minima. While noisier than batch gradient descent, it is much faster and allows for online learning from continuous data streams.
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- Which version of gradient descent performs parameter updates based on a single training example at each iteration?