100+ Deep learning MCQ Questions & Answers

Deep Learning MCQ Question and Answer

Question - 1

How many layers of Deep learning algorithms are constructed?

Solutions

Question - 2

CNN is mostly used when there is an?

structured data
unstructured data
Both A and B
None of the above

Solutions

Question - 3

Which of the following is/are Common uses of RNNs?

BusinessesHelp securities traders to generate analytic reports
Detect fraudulent credit-card transaction
Provide a caption for images
All of the above

Solutions

Question - 4

Which neural network has only one hidden layer between the input and output?

Shallow neural network
Deep neural network
Feed-forward neural networks
Recurrent neural networks

Solutions

Question - 5

RNNs stands for?

Receives neural networks
Receives neural networks
Recording neural networks
Recurrent neural networks

Solutions

Question - 6

Deep learning algorithms are _______ more accurate than machine learning algorithms in image classification.

33%
0.37%
0.4%
0.41%

Solutions

Question - 7

Which of the following is/are Limitations of deep learning?

Data labeling
Obtain huge training datasets
both 1 and 2
None of the above

Solutions

Question - 8

Which of the following is well suited for perceptual tasks?

Feed-forward neural networks
Recurrent neural networks
Convolutional neural networks
Reinforcement Learning

Solutions

Question - 9

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 the sum of p over all n equals to1?

Softmax
ReLu
Sigmoid
Tanh

Solutions

Question - 10

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 the hidden output layer and the input hidden layer?

[1 X 5] , [5 X 8]
[5 x 1] , [8 X 5]
[8 X 5] , [5 X 1]
[8 X 5] , [ 1 X 5]

Solutions

Question - 11

Which of the following statement(s) correctly represents a real neuron?

A neuron has a single input and a single output only
A neuron has multiple inputs but a single output only
A neuron has a single input but multiple outputs
All of the above statements are valid

Solutions

Question - 12

In a neural network, knowing the weight and bias of each neuron is the most important step. If you can somehow get the correct value of weight and bias for each neuron, you can approximate any function. What would be the best way to approach this?

Assign random values and pray to God they are correct
Search every possible combination of weights and biases till you get the best value
Iteratively check that after assigning a value how far you are from the best values, and slightly change the assigned values values to make them better
None of these

Solutions

Question - 13

Which of the following techniques perform similar operations as a dropout in a neural network?

Bagging
Boosting
Stacking
None of these

Solutions

Question - 14

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

Biases of all hidden layer neurons
Weight between input and hidden layer
Activation function of output layer
Weight between hidden and output layer

Solutions

Question - 15

What consists of the Boltzmann machine?

fully connected network with both hidden and visible units
asynchronous operation
stochastic update
All of the mentioned

Solutions

Question - 16

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

20×20
21×21
22×22
25×25

Solutions

Question - 17

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-

50
more than 50
less than 50
it is an arbitrary value

Solutions

Question - 18

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

Protein structure prediction
Prediction of chemical reactions
Detection of exotic particles
All of the above

Solutions

Question - 19

Assume a simple MLP model with 3 neurons and inputs=1,2,3. The weights of 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?

Solutions

Question - 20

Which, if any, of the following propositions is true about fully-connected neural networks(FCNN)?

In a FCNN, there are connections between neurons of a same layer.
A FCNN with only linear activations is a linear network.
In a FCNN, the most common weight initialization scheme is the zero initialization, because it leads to faster and more robust training.
None of these

Solutions

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