Deep Learning Intermediate Quiz 4
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Intermediate Quiz 4
1. Which is a benefit of using batch normalization?
It can speed up training and improve stability
It always increases the amount of overfitting
It replaces all activation functions in the network
2. Which task is a common application of RNNs?
Text generation
Basic image classification only
Simple linear regression on tabular data
3. What is the main advantage of data augmentation?
It increases training data variety and helps reduce overfitting
It reduces the number of input features to one
It guarantees that training will be faster in every case
4. Which method can you use to visualize training progress?
Plotting loss and accuracy curves over epochs
Printing only the model summary once
Increasing the batch size every iteration
5. What is a “learning rate schedule”?
A strategy for changing the learning rate during training
A method for splitting a dataset into classes
A specific type of nonlinear activation function
6. What is a key property of LSTM networks?
They can retain information over longer sequences
They are designed only for image inputs
They operate without any hidden state at all
7. What is the main goal of hyperparameter tuning?
To find hyperparameter values that yield better performance
To increase the number of layers regardless of results
To shrink the training dataset as much as possible
8. Which of these is a method for hyperparameter tuning?
Grid search
Random image flipping only
Adding batch normalization to every layer by default
9. What does a “flatten” layer do?
Converts a multi-dimensional tensor into a single vector
Adds a new hidden layer to the network
Reduces the batch size each epoch
10. What is a common sign of underfitting?
Both training and test accuracy remain low
Training accuracy is high but test accuracy is low
Test accuracy is high but training accuracy is low
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