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AI가 퀴즈를 학습하는 방법

지도 학습, 비지도 학습, 강화 학습, 과적합, 테스트 세트, 모델 손실이 실제로 측정하는 내용을 파악해 보세요.

관련 가이드 경로AI가 학습하는 방법
질문 1 의 8

지도 학습에서 훈련 데이터에는 무엇이 포함되나요?

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  1. 지도 학습에서 훈련 데이터에는 무엇이 포함되나요?
    • Only raw examples with no answers attached
    • Rules written by programmers for every possible situation
    • Inputs paired with target outputs, called labels
    • Rewards and penalties from a game environment
  2. 사전 정의된 카테고리 없이 유사한 쇼핑 습관으로 고객을 그룹화하는 것은 어떤 학습 유형의 예입니까?
    • Unsupervised learning
    • Rule-based programming
    • Supervised learning
    • 강화 학습
  3. 비디오 게임을 배우는 것과 같은 강화 학습 에이전트는 어떻게 개선되나요?
    • By trying actions and learning from rewards or penalties
    • By memorizing the game's source code
    • By copying labeled examples showing the correct move each turn
    • By sorting past games into clusters without feedback
  4. A model performs well on its training examples but poorly on comparable held-out examples. Which pattern does this show?
    • 과소적합
    • 과적합
    • Data leakage
    • Good generalization
  5. Why keep a test set separate from fitting the model and choosing its settings?
    • To estimate performance on examples not used for those development choices
    • To tune hyperparameters by repeatedly checking the final evaluation set
    • To add held-out examples to training after the choices are final
    • To check whether training predictions match their training labels
  6. In the guide’s supervised training loop, what does the loss measure?
    • How much electricity the training run uses
    • How long the model takes to give an answer
    • How many examples were deleted from the training dataset
    • The numerical difference between predictions and target values
  7. A text model learns to predict the next token from the preceding context. Which learning setup does the guide describe?
    • Supervised learning with separately supplied labels
    • Unsupervised clustering of the text
    • Self-supervised training that creates a prediction task from the text
    • Reinforcement learning from rewards for each token
  8. A model changes its answer after seeing an example in a prompt. What can be concluded about its learned weights?
    • They must have permanently changed
    • The prompt became part of every user’s training data
    • The model cannot use examples without retraining
    • A changed response does not by itself show a weight update