학습율,learning_rate

기호: $\eta$ 또는 $\alpha$

a hyperparameter that determines the step size in adjusting the weights
(Kwak, Slide 3, p18)
$w_{t+1}=w_t - \alpha L'(w_t)$
$0<\alpha<1$
더 섬세하고 촘촘히 : small $\alpha$
더 빠르게 : large $\alpha$ [1]
갱신하는 정도/양을 결정. 함수 값이 낮아지는 곳으로 얼마나 이동할지 즉 보폭(step size)을 정하는 역할.
너무 크거나 작으면 곤란.

MKLINK
기울기하강,gradient_descent 식에 나옴.


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