Как ИИ учится?

Ответы и пояснения появляются по мере прохождения теста.

1. Что включают в себя обучающие данные при контролируемом обучении?
   • 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. Примером какого типа обучения является группировка клиентов по схожим покупательским привычкам без каких-либо заранее определенных категорий?
   • Обучение без присмотра
   • Rule-based programming
   • Supervised learning
   • Обучение с подкреплением
3. Как улучшается агент обучения с подкреплением, например тот, кто учится играть в видеоигру?
   • 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?
   • Недооснащение
   • Переобучение
   • Утечка данных
   • 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