Podcast
Questions and Answers
What is the purpose of down-sampling in a convolutional neural network?
What is the purpose of down-sampling in a convolutional neural network?
- To reduce the network parameters and prevent overfitting (correct)
- To increase the number of network parameters
- To apply non-linearity to the convolution layer output
- To increase the size of the feature maps
What is the equation for the output of a convolution layer?
What is the equation for the output of a convolution layer?
- hk = f(Wk ∗ x - bk)
- hk = f(Wk ∗ x + bk) (correct)
- hk = Wk ∗ x - bk
- hk = Wk ∗ x + bk
What is the default stride in a CNN?
What is the default stride in a CNN?
- 1 (correct)
- 3
- 2
- 4
What is the effect of using a stride of 1 in a convolutional layer?
What is the effect of using a stride of 1 in a convolutional layer?
What is the primary purpose of padding in a CNN?
What is the primary purpose of padding in a CNN?
How does the number of filters used in a convolutional layer affect the output feature map?
How does the number of filters used in a convolutional layer affect the output feature map?
What is the purpose of the pooling function in a convolutional neural network?
What is the purpose of the pooling function in a convolutional neural network?
What determines the step size at which the network updates its parameters during training?
What determines the step size at which the network updates its parameters during training?
What is the effect of a large learning rate?
What is the effect of a large learning rate?
What is the final output of a convolutional neural network?
What is the final output of a convolutional neural network?
What determines the number of samples processed by the network in each training iteration?
What determines the number of samples processed by the network in each training iteration?
What is the trade-off when choosing a stride in a CNN?
What is the trade-off when choosing a stride in a CNN?
What is the main purpose of batch normalization in CNN architectures?
What is the main purpose of batch normalization in CNN architectures?
What is the internal covariance shift phenomenon?
What is the internal covariance shift phenomenon?
What is the effect of batch normalization on the vanishing gradient problem?
What is the effect of batch normalization on the vanishing gradient problem?
What is the benefit of batch normalization in terms of network convergence?
What is the benefit of batch normalization in terms of network convergence?
What is the relationship between batch normalization and weight initialization?
What is the relationship between batch normalization and weight initialization?
Where is the batch normalization layer typically applied in a CNN architecture?
Where is the batch normalization layer typically applied in a CNN architecture?
