Linear regression using gradient descent explained simply
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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
1:12
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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Artificial Intelligence | AI on Instagram: "Gradient descent is an optimization method used to train machine learning models by finding the set of parameters (like weights in a neural network) that make the loss function as small as possible. You can think of the loss as a surface, where each point represents a different set of parameters, and our goal is to move downhill toward the lowest point. To do this, we calculate the gradient, which is a collection of partial derivatives of the loss with
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