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DeepLearning_GPT3_questions

Pytanie 33
Which of the following is a common activation function used in convolutional neural networks?
Tanh
Softmax
Sigmoid
ReLU
Pytanie 34
What is the main advantage of using pooling layers in CNNs?
To increase the receptive field size of the network
To introduce nonlinearity into the network
To reduce the number of learnable parameters
To increase the spatial resolution of the feature maps
Pytanie 35
Which of the following is a common problem in image classification that convolutional neural networks (CNNs) aim to address?
Overfitting on small datasets
Lack of interpretability of the models
Underfitting on large datasets
Slow training times
Pytanie 36
What is dropout regularization?
It randomly removes a fraction of the neurons from the network during training.
It adds the sum of absolute values of the weights as a penalty term to the loss function.
It adds the sum of squared values of the weights as a penalty term to the loss function.
It adds a Gaussian noise term to the weights during training.
Pytanie 37
What is L2 regularization?
It adds the maximum absolute value of the weights as a penalty term to the loss function.
It adds the maximum squared value of the weights as a penalty term to the loss function.
It adds the sum of absolute values of the weights as a penalty term to the loss function.
It adds the sum of squared values of the weights as a penalty term to the loss function.
Pytanie 38
What is the effect of increasing the regularization parameter in L2 regularization?
It reduces the number of non-zero weights.
It has no effect on the weights.
It increases the magnitude of the weights.
It reduces the magnitude of the weights.
Pytanie 39
What is weight decay?
It adds the sum of squared values of the w loss function.
It adds the sum of absolute values of the weights as a penalty term to the loss function.
It adds a Gaussian noise term to the weights during training.
It stops training the network when the validation error stops decreasing.
Pytanie 40
Which of the following is a technique used for data augmentation as a regularization technique?
Adding noise to the input data
Subtracting noise from the input data
Removing a random subset of the input data
None of the above
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