Deep learning

Q1. Which layer is the Input layer?

first layer
second layer
last layer
middle layer

Q2. 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?

ReLu
Tanh
Softmax
Sigmoid

Q3. CNNs are suited for

recurrent neural network
perceptual tasks
None of these
Both

Q4. In which of the following applications can we use deep learning to solve the problem?

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

Q5. Number of layers of deep learning algorithm

4
5
3
2

Q6. Which of following activation functions can’t be used as output layer to classify an image ?

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

Q7. Deep learning is

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

Q8. ____________ is a multi-layered neural network that can store information in context nodes

All of these
RNA
RNN
RNS

Q9. 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

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

Q10. Which of the following statements is true when you use 1×1 convolutions in a CNN?

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

Q11. 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

Q12. CNN

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

Q13. 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?

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

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

Machine learning
Computer
None of these
Both

Q15. 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]
[8 X 5] , [5 X 1]
[1 X 5] , [5 X 8]
[5 x 1] , [8 X 5]

Q16. Which of the following neural network training challenges can be solved using batch normalization?

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

Q17. What are the layers in between called?

Hidden layers
None of these
Middle layers
In-between layers

Q18. Which of the following are universal approximators?

Neural Networks
Boosted Decision Trees
Kernel SVM
All of the above

Q19. Uses of RNN

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

Q20. Deep learning teaches computer to

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

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Certificate in Deep Learning

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