Gradient descent
An iterative optimization method that adjusts model parameters in the direction that reduces a loss function, scaled by a learning rate. Too high a learning rate overshoots and can diverge, too low a rate converges so slowly that training looks stalled.
Why exams ask this
Tested as a learning-rate scenario: loss that explodes or oscillates points at a rate too high, loss that barely moves points at a rate too low. The exam wants the learning rate identified as the lever, not the optimizer algorithm itself.
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