Deep Learning MCQ Questions and Answers PDF Download

Deep Learning MCQ Questions With Answer | Deep Learning Solved MCQ Questions and Answer | Deep Learning MCQ PDF

Deep Learning MCQ: Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. The field of Artificial Intelligence requires many in demand skills like deep learning, reinforcement learning, computer vision, natural language processing, robotics, etc. Candidates with required skills to apply for this relevant jobs. Candidates may practice these Deep Learning MCQ Questions and answers and it will improve your knowledge as well as skills. In future, there are vast job opportunities are available for this Field. In this section, we have made 30 Deep Learning MCQ with answer. Aspirants read the given Deep Learning questions and choose the correct answer for every questions. As part of this, you can download the Deep Learning Multiple choice questions and answers PDF. There will be no cost for this Deep Learning MCQ PDF.

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Deep Learning MCQ With Answer

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

1. Deep neural network
2. Feed-forward neural networks
3. Recurrent neural networks
4. Shallow neural network

Answer: Shallow neural network

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

1. 0.37%
2. 0.41%
3. 33%
4. 0.4%

Answer: 0.41%

3. How many layers of Deep learning algorithms are constructed?

1. 3
2. 4
3. 2
4. 5

Answer: 4

4. RNNs stands for?

1. Receives neural networks
2. Recurrent neural networks
3. Recording neural networks
4. None pf above

Answer: Recurrent neural networks

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

1. Obtain huge training datasets
2. Data labeling
3. None of the above
4. All of above

Answer: All of above

6. Which of the following is well suited for perceptual tasks?

1. Recurrent neural networks
2. Convolutional neural networks
3. Reinforcement Learning
4. Feed-forward neural networks

Answer: Convolutional neural networks

7. CNN is mostly used when there is an?

1. unstructured data
2. structured data
3. None of the above
4. All of above

Answer: structured data

8. 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?

1. ReLu
2. Softmax
3. Tanh
4. Sigmoid

Answer: Softmax

9. 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?

1. Search every possible combination of weights and biases till you get the best value
2. 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
3. Assign random values and pray to God they are correct
4. None of these

Answer: 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

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

1. Detect fraudulent credit-card transaction
2. Provide a caption for images
3. Businesses Help securities traders to generate analytic reports
4. All of the above

Answer: All of the above

11. 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. [5 x 1] , [8 X 5]
2. [1 X 5] , [5 X 8]
3. [8 X 5] , [ 1 X 5]
4. [8 X 5] , [5 X 1]

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

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

1. Boosting
2. Bagging
3. Stacking
4. None of above

Answer: Bagging

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

1. A neuron has multiple inputs but a single output only
2. A neuron has a single input and a single output only
3. All of the above
4. A neuron has a single input but multiple outputs

Answer: All of the above

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

1. Weight between input and hidden layer
2. Weight between hidden and output layer
3. Biases of all hidden layer neurons
4. Activation function of output layer

Answer: Weight between input and hidden layer

15. 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__________

1. more than 50
2. less than 50
3. it is an arbitrary value
4. 50

Answer: 50

16. 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?

1. 64
2. 128
3. 32
4. 96

Answer: 96

17. 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?

1.  2
2. 21×21
3. 20×20
4. 25×25

Answer: 22×22

18. What consists of the Boltzmann machine?

1. asynchronous operation
2. fully connected network with both hidden and visible units
3. stochastic update
4. All of the above

Answer: All of the above

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

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

Answer: A FCNN with only linear activations is a linear network

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

1. Prediction of chemical reactions
2. Protein structure prediction
3. Detection of exotic particles
4. All of the above

Answer: All of the above

21. SVM stands for_______

1. Support Vector Mechanism
2. Super Visual Machine
3. Support Vector Machine
4. Support Vector Model

Answer: Support Vector Machine

22. Which statement is true?

1. Deep learning is an analogue framework for learning representations from data
2. Deep learning is a mathematical framework for learning representations from data
3. Deep learning is a biological framework for learning representations from brain
4. Deep learning is a digital framework for learning representations from data

Answer: Deep learning is a mathematical framework for learning representations from data

23. Three technical forces are driving advances in machine learning________

1. Pen and a piece of paper
2. Hardware, Datasets & benchmarks, and Algorithmic advances
3. Super computers only
4. All of the above

Answer: Hardware, Datasets & benchmarks, and Algorithmic advances

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

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

Answer: All of the above

25. In which neural net architecture, does weight sharing occur?

1. Recurrent Neural Network
2. Convolutional neural Network
3. Fully Connected Neural Network
4. Both 1 and 2

Answer: Both 1 and 2

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

1. Numpy
2. Deep Learning
3. Scipy
4. All of the above

Answer: Deep Learning

27. The two key ideas of deep learning for computer vision________

1. Support Vector Machines and loss function
2. Convolutional neural networks and backpropagation
3. Deep neural networks and kernel functions
4. None

Answer: Convolutional neural networks and backpropagation

28. What is loss function in deep learning?

1. To control the output of a neural network, you need to be able to measure how far this output is from what you expected
2. To calculate loss in banks
3. These are the predicted values only
4. These are true targets of data

Answer: To control the output of a neural network, you need to be able to measure how far this output is from what you expected

29. What is shallow learning in deep learning?

1. Machine learning tend to focus on learning 512 layers of representations of the data
2. Machine learning tend to focus on learning only one or two layers of representations of the data
3. Machine learning tend to focus on learning 64 layers of representations of the data
4. Machine learning tend to focus on learning 10 layers of representations of the data

Answer: Machine learning tend to focus on learning only one or two layers of representations of the data

30. What is representation in deep learning?

1. It gets closer to the expected output
2. It is a way to look at data to represent or encode
3. RGB and HSV are two different examples of representations
4. All of the above

Answer: All of the above

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