1. Which of the following is a subset of machine learning? 

A . Neural Network

B . Perceptron

C . Deep Learning

D . All of the above

Answer

2. 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

Answer

3. The number of nodes in the input layer is 20 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are

A . 100

B . less than 100

C . more than 100

D . It is an arbitrary value

Answer

4. 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

Answer

5. 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

Answer

6. In a classification problem, which of the following activation function is most widely used in the hidden layer of neural networks?

A . Sigmoid function

B . Hyperbolic function

C . Rectifier function

D . All of the above.

Answer

7. Which of the following is true about bias?

A . Bias is inherent in any predictive model

B . Bias impacts the output of the neurons

C . Both A and B

D . None

Answer

8. What is the purpose of a loss function?

A . Calculate the error value of the forward network

B . Optimize the error values according to the error rate

C . Both A and B

D . None

Answer

9. Which of the following is a loss function?

A . Sigmoid function

B . Cross entropy

C . ReLu

D . All of the above

Answer

10. Which of the following loss function is used in regression?

A . Logarithmic loss

B . Cross entropy

C . Mean squared error

D . None

Answer

11. What is gradient descent?

A . Activation function

B . Loss function

C . Optimization algorithm

D . None

Answer

12. What does a gradient descent algorithm do?

A . Tries to find the parameters of a model that minimizes the cost function

B . Adjusts the weights at the input layers

C . Both A and B

D . None

Answer

13. Which of the following activation function can not be used in the output layer of an image classification model?

A . ReLu

B . Softmax

C . Sigmoid

D . None

Answer

14. For a binary classification problem, which of the following activation function is used?

A . ReLu

B . Softmax

C . Sigmoid

D . None

Answer

15. Types of Cost Function

A . Regression Cost Function

B . Binary Classification cost Functions

C . Multi-class Classification Cost Function.

D . All the above

Answer

Fill in the Blanks

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16. If you increase the number of hidden layers in a Multi Layer Perceptron, the classification error of test data always decreases.(True/ False)

Answer

17. _____________ is a subset of Machine Learning that uses mathematical functions to map the input to the output.

Answer

18. A ______________ is an important parameter that determines how well a machine learning model performs for a given dataset

Answer

19. __________ Functions are most often used as the output of a classifier, to represent the probability distribution over n different classes

Answer

20. Rectified linear units are an excellent default choice of _________ layer.

Answer

21. Sigmoid and tanh activation functions cannot be with many layers due to the ____________ problem.

Answer

22. __________ function overcomes the vanishing gradient problem, allowing models to learn faster and perform better

Answer

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☞ Neural Networks and Deep Learning-Unit-1 Objective Type Questions - [ NNDL ]

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