NPTEL Deep Learning – IIT Ropar Week 3 Assignment Answers 2024
1. Choose the correct dimensions of W1 and a1
- W1∈R6×3
- W1∈R3×6
- W1∈R1×6
- a1∈R6×1
- a1∈R1×6
- a1∈R1×1
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2. How many learnable parameters(including bias) are there in the network?
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3. Which of the following loss functions can be used for the classification problem?
- Means Squared error
- Cross entropy
- Both Mean Squared Error and Cross Entropy loss can be used
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4. We have a multi-classification problem that we decide to solve by training a feedforward neural network. What activation function should we use in the output layer to get the best results?
- Logistic
- Step function
- Softmax
- linear
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5. We have data x with the following labels y = [‘car’, ‘bus’, ‘bike’, ‘car’, ‘bus’, ‘car’, ‘bus’, ‘bike’, ‘car’]. Which of the following distribution will give the lowest cross-entropy loss with y? (Distribution is given in the following order [‘car’, ‘bus’,‘bike’])?
- [0.49,0.30,0.21]
- [0.44,0.33,0.22]
- [0.50,0.30,0.20]
- [0.44,0.39,0.19]
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6. Which of the following statements about backpropagation is true?
- It is used to compute the output of a neural network.
- It is used to initialize the weights in a neural network.
- It is used to optimize the weights in a neural network.
- It is used to regularize the weights in a neural network.
Answer :-
7. Given two probability distributions p and q, under what conditions is the cross entropy between them minimized?
- All the values in p are lower than corresponding values in q
- All the values in p are lower than corresponding values in q
- p=0(0 is a vector)
- p=q
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8. Given that the probability of Event A occurring is 0.80 and the probability of Event B occurring is 0.20, which of the following statements is correct?
- Event A has a low information content
- Event A has a high information content
- Event B has a low information content
- Event B has a high information content
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9. What is the predicted output for the given input x after doing the forward pass? (Choose the option closest to your answer)
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10. Compute and enter the loss between the output generated by input x and the true output y
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