گائیڈ سے منسلک کوئز · آسان سطح
AI کوئز کیسے سیکھتا ہے۔
زیر نگرانی، غیر زیر نگرانی اور کمک سیکھنے، نیز اوور فٹنگ، ٹیسٹ سیٹس، اور ماڈل کے نقصان کی حقیقت میں کیا پیمائش ہوتی ہے اس کی اپنی گرفت کو چیک کریں۔
سوال 1 کی 8
زیر نگرانی سیکھنے میں، تربیتی ڈیٹا میں کیا شامل ہے؟
ہر سوال کا جائزہ لیں۔8 سوالات
جب آپ کوئز دیتے ہیں تو جوابات اور وضاحتیں ظاہر ہوتی ہیں۔
- زیر نگرانی سیکھنے میں، تربیتی ڈیٹا میں کیا شامل ہے؟
- 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
- بغیر کسی پیش وضاحتی زمرے کے اسی طرح کی خریداری کی عادات کے مطابق گاہکوں کو گروپ کرنا کس قسم کے سیکھنے کی ایک مثال ہے؟
- غیر زیر نگرانی تعلیم
- Rule-based programming
- Supervised learning
- کمک سیکھنے
- ایک کمک سیکھنے والا ایجنٹ، جیسا کہ کوئی ویڈیو گیم کھیلنا سیکھتا ہے، کیسے بہتر ہوتا ہے؟
- 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
- A model performs well on its training examples but poorly on comparable held-out examples. Which pattern does this show?
- انڈر فٹنگ
- اوور فٹنگ
- Data leakage
- Good generalization
- 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
- 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
- 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
- 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