Deep Learning Intermediate Quiz 3
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Intermediate Quiz 3
1. What is meant by “early stopping”?
Ending training when validation performance stops improving
Always stopping after exactly one epoch
Halting training whenever accuracy reaches 100% on training data
2. What is a hyperparameter in deep learning?
A setting chosen before training, such as learning rate or batch size
A weight value learned directly from the data
A special neuron that never changes
3. Which of these is a regularization method?
L2 weight penalty
Batch normalization only
Adding extra labels to the dataset
4. What is the primary role of a validation set?
To tune model settings and monitor overfitting
To provide extra data for final training only
To store the trained model weights
5. How do training and test sets differ in supervised learning?
Training is for learning; testing is for evaluating on unseen data
Training is for tuning hyperparameters; testing is also for training
Both sets are always used to update model weights
6. Which situation indicates overfitting?
Very high training accuracy but low test accuracy
High test accuracy but low training accuracy
Low accuracy on both training and test sets
7. What is a “mini-batch” in training?
A small subset of the data used for a single parameter update
A type of optimizer based on full batches
A dedicated layer for batch normalization
8. Which is a benefit of using transfer learning?
Faster training and better performance with less labeled data
The model always needs more data than training from scratch
It consistently forces the model to overfit
9. What is the purpose of a validation curve?
To show model performance as a function of a hyperparameter
To visualize the architecture of the network
To display examples from the dataset only
10. In a convolutional layer, what does padding do?
Adds extra pixels around the input to control output size
Removes neurons that are not used frequently
Increases the batch size during training
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