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Insightforge | AI & Data Science on Instagram: "Double descent describes a surprising pattern where a model’s test error drops, then rises, and then drops again as the model becomes more complex. Traditionally, as complexity increases, a model reduces bias at first but eventually begins to overfit, causing the test error to rise. Modern research has shown that after this overfitting spike, the test error can actually fall again, creating the double-descent curve. A simple example is fitting poly
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Insightforge | AI & Data Science on Instagram: "In three dimensions, linear regression can be visualized as a plane that best fits the data. When we move into higher dimensions, this idea extends to an n-dimensional hyperplane that represents the model’s predictions. Training this model relies on calculus and linear algebra, along with a way to measure how well the hyperplane matches the data. This measurement is the loss function, most commonly the mean squared error (MSE) or similar variants.
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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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