Gradient Descent

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Gradient descent is an optimization technique that helps an AI model learn by minimizing its loss function, a measure of how far the model’s predictions are from the correct answers. It does this by repeatedly updating the model’s parameters, moving them step by step in the direction that lowers the loss. The size of each step is controlled by the learning rate: too large and the model might skip over the best solution, too small and training can take forever.

Most modern models use mini-batch or stochastic gradient descent, which estimate updates from small batches of data to train more efficiently. Gradient descent is the foundation of most machine learning and deep learning models because it scales effectively to large datasets and complex problems.

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