역전파 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. What quantity does backpropagation compute for a neural network's relevant weights?
   • Derivatives of the loss with respect to those weights
   • A causal percentage of total error uniquely assigned to each weight
   • The architecture drawing without any calculation
   • A permanently updated set of weights without an optimizer
2. 역전파의 핵심은 어떤 수학적 규칙인가요?
   • 피타고라스의 정리
   • 베이즈의 정리
   • 미적분의 연쇄 법칙
   • 이차 공식
3. Why can reverse-mode backpropagation compute many parameter derivatives efficiently?
   • It avoids all local derivatives
   • It reuses graph structure and relevant intermediate results
   • It calculates only the output prediction again
   • It guarantees every implementation has exactly one forward pass of runtime
4. 역전파는 어떤 순서로 기울기를 계산합니까?
   • 입력층에서 출력층으로
   • 순서 없이 모든 레이어를 동시에
   • 중간 히든 레이어에만 해당
   • 출력층에서 뒤쪽으로 입력층으로
5. Does computing backpropagation gradients by itself establish that model weights were updated?
   • Yes; every backward computation automatically applies a learning rate
   • Yes; gradient values always replace the weights directly
   • No; the optimizer update is a separate step
   • Only if the network has no parameters
6. In a conventional cached-backpropagation workflow, what does the forward computation provide for later derivative calculations?
   • Relevant intermediate values that can be retained for the backward computation
   • A completed optimizer update without any update rule
   • A guarantee that all later evaluation errors disappear
   • A permanent deletion of the training objective
7. On one composed backpropagation path, local scalar derivatives are three and four. What is their chain-rule product?
   • 세븐
   • 하나
   • Three quarters
   • 12
8. A shared parameter has loss-gradient contributions two and five through two paths. What combined contribution is accumulated?
   • 10
   • 세븐
   • 세
   • Two fifths