Simplified Gradient Descent Optimization
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Simplified Gradient Descent Optimization
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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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AI | Machine Learning | Tech on Instagram: "Stochastic Gradient Descent (SGD) is an optimization method used to minimize a function, usually a loss function, to improve a model. Think of it like trying to get to the lowest point of a hill: the gradient represents the slope, telling you which direction to move to go down. Instead of looking at the entire hill (or dataset), SGD picks small random subsets of data at each step, calculating the gradient based on that subset. This makes updates faster
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