The Math You Missed Behind Gradient Descent
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The Math You Missed Behind Gradient Descent
Four lines of code can train a neural network. Initialize the weights. Compute the gradient. Step in the opposite direction. Repeat. Do it for enough times, and somehow the model learns. But if you think about it carefully, that should feel strange. The loss landscape is high-dimensional, curved, full of saddle points, and often non-convex. The ...
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