Deep Learning Questions And Answers
11. In which of the following applications can we use deep learning to solve the problem?
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?
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
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?
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?
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?
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 ?
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?
Explanation: Weights between input and hidden layer are constant.
19. Sentiment analysis using Deep Learning is a many-to one prediction task
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?
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