## 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

View Answer

Ans : D

Explanation: We can use neural network to approximate any function so it can theoretically be used to solve any problem.

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

View Answer

Ans : D

Explanation: 1×1 convolutions are called bottleneck structure in CNN.

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

View Answer

Ans : A

Explanation: Since MLP is a fully connected directed graph, the number of connections are a multiple of number of nodes in input layer and hidden layer.

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

View Answer

Ans : C

Explanation: The size of the convoluted matrix is given by C=((I-F+2P)/S)+1, where C is the size of the Convoluted matrix, I is the size of the input matrix, F the size of the filter matrix and P the padding applied to the input matrix. Here P=0, I=28, F=7 and S=1. There the answer is 22.

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]

View Answer

Ans : B

Explanation: The size of weights between any layer 1 and layer 2 Is given by [nodes in layer 1 X nodes in layer 2].

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

View Answer

Ans : A

Explanation: Softmax function is of the form in which the sum of probabilities over all k sum to 1.

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

View Answer

Ans : C

Explanation: The output will be calculated as 3(1*4+2*5+6*3) = 96

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

View Answer

Ans : A

Explanation: Weights between input and hidden layer are constant.

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

View Answer

Ans : A

Explanation: Option A is correct. This is because from a sequence of words, you have to predict whether the sentiment was positive or negative.

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

A. True

B. False

C. Can be true and false

D. Can not say

View Answer

Ans : B

Explanation: This is not always true. If we have a max pooling layer of pooling size as 1, the parameters would remain the same.s

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