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DeepLearning_GPT3_questions

Pytanie 65
What is the purpose of the forget gate in an LSTM cell?
To control how much of the cell state is updated
To decide whether to update the cell state or not
To determine the input to the output gate
To determine the output of the LSTM cell
Pytanie 66
Which of the following is NOT a type of gate in an LSTM?
Output gate
Input gate
Forget gate
Update gate
Pytanie 67
What is the purpose of the teacher forcing technique in training RNNs?
To provide the network with the correct input at each time step during training.
To speed up the convergence of the network.
To improve the generalization ability of the network.
To prevent overfitting.
Pytanie 68
What is the difference between a unidirectional and bidirectional RNN?
A unidirectional RNN can process data in both directions, while a bidirectional RNN can only process data in one direction.
A unidirectional RNN can only process data in one direction, while a bidirectional RNN can process data in both directions.
Both unidirectional and bidirectional RNNs can process data in both directions
Both unidirectional and bidirectional RNNs can only process data in one direction.
Pytanie 69
What is the long short-term memory (LSTM) architecture designed to address in RNNs?
The underfitting problem.
The overfitting problem.
The vanishing gradient problem.
The exploding gradient problem.
Pytanie 70
What is the vanishing gradient problem in recurrent neural networks (RNNs)?
The weights of the network become too small.
The weights of the network become too large.
The gradients become too large during backpropagation.
The gradients become too small during backpropagation.
Pytanie 71
Which of the following is a potential application of UNet in medical image analysis?
Detecting anomalies in a time series
Segmenting tumor regions in an MRI
All of the above
Identifying objects in an image
Pytanie 72
How does UNet handle class imbalance in image segmentation tasks?
By weighting the loss function for underrepresented classes
UNet does not handle class imbalance
By oversampling the minority classes
By undersampling the majority classes
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