Lecture 25: Stochastic Gradient Descent | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Mathematics | MIT OpenCourseWare
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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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Insightforge | AI & Data Science on Instagram: "Stochastic Gradient Descent (SGD) is an optimization technique used to reduce a model’s loss and improve its performance. A good way to imagine it is like walking down a hill to reach the lowest point. The gradient tells you the slope and the direction to move downward. But instead of looking at the entire landscape (the full dataset), SGD looks at small random batches of data to estimate the slope at each step. Because it works with smaller sample
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