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WorksheetsDeep Learning Batch 2
Total questions: 10
Worksheet time: 2mins
What is the primary goal of optimization in deep learning?
A) To minimize the training error
B) To maximize the generalization error
C) To increase the model complexity
) To reduce the number of layers
Which of the following is a common issue with high variance in a model?
A) Underfitting
B) Overfitting
C) High bias
D) Low accuracy on training data
What is the main advantage of Mini-Batch Gradient Descent over Batch Gradient Descent?
A) It requires less memory
B) It converges faster for large datasets
C) It is less noisy
D) It always reaches the global minimum
Which of the following is true about the bias-variance trade-off?
A) Increasing model complexity reduces bias but increases variance
B) Increasing model complexity reduces both bias and variance
C) Decreasing model complexity reduces bias but increases variance
D) Decreasing model complexity reduces both bias and variance
What is the primary purpose of early stopping in deep learning?
A) To reduce the number of layers in the network
B) To stop training when the validation error starts increasing
C) To increase the learning rate
D) To reduce the number of parameters in the model
Which of the following is true about the Adagrad optimizer?
A) It uses a fixed learning rate for all parameters
B) It is less efficient than SGD
C) It adapts the learning rate based on the history of gradients
D) It does not use momentum
What is the main challenge in deep learning related to data?
A) Lack of computational power
B) Overfitting due to small datasets
C) The need for large amounts of data
D) The complexity of neural networks
Which of the following is a common technique for hyperparameter optimization?
A) Grid search
B) Early stopping
C) Dropout
D) Data augmentation
What is the primary purpose of data augmentation in deep learning?
A) To reduce the number of layers in the network
B) To increase the size of the training dataset artificially
C) To reduce the learning rate
D) To increase the model complexity
Which of the following is true about the RMSprop optimizer?
A) It uses a fixed learning rate for all parameters
B) It adapts the learning rate based on the magnitude of recent gradients
C) It is less efficient than SGD
D) It does not use momentum
