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DeepLearning_GPT3_questions

Wyświetlane są wszystkie pytania.
Pytanie 57
What is the main disadvantage of Adagrad?
It requires a large amount of memory
It can lead to overfitting
It can get stuck in local optima
It can be slow to converge
Pytanie 58
What is the advantage of using Adagrad over other optimization algorithms?
It is computationally efficient
It can adapt the learning rate for each parameter separately
It requires less memory
It is less prone to getting stuck in local optima
Pytanie 59
How does Adagrad adapt the learning rate for each parameter?
It randomly selects a learning rate for each parameter during each iteration
It sets a fixed learning rate for all parameters
It uses a moving average of the past gradients for each parameter to scale the learning rate
It updates the learning rate based on the gradient of the entire batch
Pytanie 60
What is Adagrad?
A regularization technique for reducing overfitting
An optimization algorithm for training deep learning models
A type of activation function used in neural networks
A loss function for measuring the difference between predicted and actual values
Pytanie 61
What is the purpose of the peephole connections in a peephole LSTM?
To allow the gates to observe the input sequence directly
To allow the gates to observe the previous hidden state directly
To allow the gates to observe the current cell state directly
To allow the gates to adjust their activations based on the previous time step
Pytanie 62
Which of the following is true about the output of an LSTM cell?
The output is always the same as the hidden state
The output is a function of both the hidden state and the cell state
The output is determined by the output gate
The output is determined by the forget gate
Pytanie 63
What is the difference between a standard LSTM cell and a peephole LSTM cell?
Peephole LSTM cells do not have an output gate
Peephole LSTM cells use a different activation function
Peephole LSTM cells have additional connections from the cell state to the gates
Peephole LSTM cells have an extra forget gate
Pytanie 64
Which of the following is an advantage of using LSTM over traditional recurrent neural networks (RNNs)?
LSTMs can handle variable-length sequences
LSTMs converge faster than RNNs
LSTMs require fewer parameters than RNNs
LSTMs are less prone to overfitting than RNNs
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