Stochastic Gradient Descent (SGD)
An optimization method that estimates gradients using mini-batches to update parameters iteratively.
SGD updates neural parameters by evaluating loss on small, randomly sampled mini-batches. It is a fundamental optimization technique that scales well to large datasets.
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