Deep Learning Questions And Answers

11. In which of the following applications can we use deep learning to solve the problem?

A. Protein structure prediction
B. Prediction of chemical reactions
C. Detection of exotic particles
D. All of the above

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12. Which of the following statements is true when you use 1×1 convolutions in a CNN?

A. It can help in dimensionality reduction
B. It can be used for feature pooling
C. It suffers less overfitting due to small kernel size
D. All of the above

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13. The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are

A. 50
B. less than 50
C. more than 50
D. It is an arbitrary value

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14. The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 7 X 7 with a stride of 1. What will be the size of the convoluted matrix?

A. 20x20
B. 21x21
C. 22x22
D. 25x25

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15. In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden output layer and input hidden layer?

A. [1 X 5] , [5 X 8]
B. [5 x 1] , [8 X 5]
C. [8 X 5] , [5 X 1]
D. [8 X 5] , [ 1 X 5]

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16. Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes (p1, p2..pk) such that sum of p over all n equals to 1?

A. Softmax
B. ReLu
C. Sigmoid
D. Tanh

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17. Assume a simple MLP model with 3 neurons and inputs= 1,2,3. The weights to the input neurons are 4,5 and 6 respectively. Assume the activation function is a linear constant value of 3. What will be the output ?

A. 32
B. 64
C. 96
D. 128

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18. Which of the following would have a constant input in each epoch of training a Deep Learning model?

A. Weight between input and hidden layer
B. Weight between hidden and output layer
C. Biases of all hidden layer neurons
D. Activation function of output layer

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19. Sentiment analysis using Deep Learning is a many-to one prediction task

A. True
B. False
C. Can be true and fasle
D. can not say

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20. In CNN, having max pooling always decrease the parameters?

A. True
B. False
C. Can be true and false
D. Can not say

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