What distinguishes 'stochastic gradient descent' (SGD) from batch gradient descent?
- SGD uses no data
- SGD uses one/few samples per update
- SGD never converges
- SGD ignores gradients
Explanation
SGD updates
weights using one or a few samples per step, speeding training.
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