Deep learning

Q1. In which of the following applications can we use deep learning to solve the problem?

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

Q2. CNN

covolutional net network
converted neural network
None of these
convolutional neural network

Q3. Deep learning is

computer software that mimics the network of neurons in a brain
subset of machine learning
Both
None of these

Q4. Which of following activation functions can’t be used as output layer to classify an image ?

ReLU
If(x>5,1,0)
Sigmoid
Tanh

Q5. Uses of RNN

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

Q6. Which of the following would have a constant input in each epoch of training a Deep Learning model?

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

Q7. Number of layers of deep learning algorithm

5
4
3
2

Q8. Which of the following neural network training challenges can be solved using batch normalization?

Restrict activations to become too high or low
Overfitting
Training is too slow
Both B and C

Q9. 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?

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

Q10. Which of the following are universal approximators?

Boosted Decision Trees
All of the above
Neural Networks
Kernel SVM

Q11. Which of the following statements is true when you use 1×1 convolutions in a CNN?

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

Q12. ____________ is a multi-layered neural network that can store information in context nodes

RNA
RNS
RNN
All of these

Q13. Deep learning teaches computer to

None of these
learn by example
Both
to perform classification tasks directly from images, text, or sound

Q14. ________ is the study of computer algorithms that learn through data

None of these
Computer
Machine learning
Both

Q15. Which layer is the Input layer?

middle layer
first layer
second layer
last layer

Q16. 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?

Sigmoid
Tanh
Softmax
ReLu

Q17. CNNs are suited for

perceptual tasks
Both
recurrent neural network
None of these

Q18. 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?

28 X 28
22 X 22
21 X 21
7 X 7

Q19. 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

it's an arbitrary value
50
more than 50
less than 50

Q20. What are the layers in between called?

Hidden layers
Middle layers
None of these
In-between layers

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