NPTEL Deep Learning – IIT Ropar Week 11 Assignment Answers 2024
1. Select the correct statements about GRUs
- GRUs have fewer parameters compared to LSTMs
- GRUs use a single gate to control both input and forget mechanisms
- GRUs are less effective than LSTMs in handling long-term dependencies
- GRUs are a type of feedforward neural network
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2. What is the main advantage of using GRUs over traditional RNNs?
- They are simpler to implement
- They solve the vanishing gradient problem
- They require less computational power
- They can handle non-sequential data
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3. What is the role of the forget gate in an LSTM network?
- To determine how much of the current input should be added to the cell state
- To determine how much of the previous time step’s cell state should be retained
- To determine how much of the current cell state should be output
- To determine how much of the current input should be output
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4. How does LSTM prevent the problem of vanishing gradients?
- Different activation functions, such as ReLU, are used instead of sigmoid in LSTM
- Gradients are normalized during backpropagation
- The learning rate is increased in LSTM
- Forget gates regulate the flow of gradients during backpropagation
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5. We construct an RNN for the sentiment classification of text where a text can have positive sentiment or negative sentiment. Suppose the dimension of one-hot encoded-words is R100×1, dimension of state vector si is R50×1. What is the total number of parameters in the network? (Don’t include biases also in the network)
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6. Arrange the following sequence in the order they are performed by LSTM at time step t.
[Selectively read, Selectively write, Selectively forget]
- Selectively read, Selectively write, Selectively forget
- Selectively write, Selectively read, Selectively forget
- Selectively read, Selectively forget, Selectively write
- Selectively forget, Selectively write, Selectively read
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7. Which of the following is a limitation of traditional feedforward neural networks in handling sequential data?
- They can only process fixed-length input sequences
- They are highly optimizable using the gradient descent methods
- They can’t model temporal dependencies between sequential data
- All of These
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8. Which of the following is a formula for computing the output of an LSTM cell?
- ot=σ(Wo[ht−1,xt]+bo)
- ft=σ(Wf[ht−1,xt]+bf)
- ct=ft∗ct−1+it∗gt
- ht=ot∗tanh(ct)
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9. Which type of neural network is best suited for processing sequential data?
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Fully Connected Neural Networks (FCN)
- Deep Belief Networks (DBN)
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10. Which of the following is true about LSTM and GRU networks?
- LSTM networks have more gates than GRU networks
- GRU networks have more gates than LSTM networks
- LSTM and GRU networks have the same number of gates
- Both LSTM and GRU networks have no gates
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