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Stochastic gradient descent
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726.
SGD vs Batch Gradient Descent
easy
How does stochastic gradient descent (SGD) differ from batch gradient descent?
A
It computes the gradient using a single randomly selected sample per update rather than the full dataset
B
It computes the gradient using a fixed random subset of samples selected once before training begins
C
It computes the gradient using all samples but applies updates only when the loss improves significantly
D
It computes the gradient analytically using the loss function's closed-form derivative at each step
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