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DeepLearning_GPT3_questions

Wyświetlane są wszystkie pytania.
Pytanie 25
What is the purpose of regularization in deep learning?
To increase the accuracy of the model
To reduce the bias in the model
To reduce the variance in the training data
To prevent overfitting
Pytanie 26
What is the purpose of using dropout in convolutional neural networks?
To increase the accuracy of the model
To increase the number of parameters in the network
To prevent overfitting in the model
To reduce the computational cost of the model
Pytanie 27
What is the purpose of using padding in convolutional neural networks?
To increase the number of filters in the convolutional layer
To reduce overfitting in the model
To ensure that the output volume has the same spatial dimensions as the input volume
To reduce the spatial dimensions of the input volume
Pytanie 28
Which of the following is used to reduce the spatial dimensions of the input volume in a convolutional neural network?
Activation functions
Fully connected layers
Pooling layers
Convolutional layers
Pytanie 29
What is the output shape of a convolutional layer with 32 filters, a filter size of 3x3, and input shape of 224x224x3?
32x32x3
222x222x32x3
222x222x32
222x222x3
Pytanie 30
Which of the following statements is true about convolutional neural networks?
The use of convolutional layers reduces the number of parameters in the network.
Convolutional neural networks cannot be used for object detection tasks.
Convolutional neural networks can only be used for image classification tasks.
Convolutional layers are always followed by fully connected layers.
Pytanie 31
Which of the following is a common technique used to prevent overfitting in convolutional neural networks?
All of the above
Dropout
Data augmentation
Early stopping
Pytanie 32
What is the main advantage of using convolutional layers in CNNs?
To increase the receptive field size of the network
To increase the number of learnable parameters
To introduce nonlinearity into the network
To reduce the spatial resolution of the input
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