Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is a powerful optimization technique used in machine learning to minimize the loss function, which measures the difference between the predicted and actual values. Unlike traditional gradient descent, which updates model parameters using the entire dataset, SGD updates the parameters using a randomly selected subset, or mini-batch, of data. This stochastic approach allows for faster iterations and can help the model escape local minima, leading to more efficient convergence, especially when dealing with large datasets.

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