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Gradient descent is one of the core optimization methods that helps AI models learn by minimizing a loss function, which measures how far predictions are from the true targets. You can think of the loss function as a hilly landscape, often called the loss landscape, where high points represent large errors and low points represent small errors. At any location on this landscape, the gradient tells us the direction in which the loss increases the fastest. Gradient descent takes this gradient and
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Gradient descent is one of the core optimization methods that helps AI models learn by minimizing a loss function, which measures how far predictions are from the true targets. You can think of the loss function as a hilly landscape, often called the loss landscape, where high points represent large errors and low points represent small errors. At any location on this landscape, the gradient tells us the direction in which the loss increases the fastest. Gradient descent takes this gradient and
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