Artificial Intelligence For Engineering Assignment 7 | 1st year | aktu by Professor
uploaded:: 11:48:33am 2 Jun 2021
Artificial Intelligence For Engineering Assignment 7 | 1st year | aktu
Q:1. Applications of Deep Learning are:
1.Self-driving cars
2.Fake news detection
3.Virtual Assistants
4.All the above
Solution- 4. All the above
Q:2. The inputs for a single layer neural network are 1, 3, 2 and the weights of links connecting input neurons to the output neuron are 2, 2, and 3 then the output will be (Identity activation function is used in output neuron):
1.6
2.14
3.12
4.None of the above
Solution- 2. 14
Q:3. Which of the following is not a type of Artificial Neural Network?
1.Perceptron
2.Radial Basis Functions
3.Random Forest
4.Autoencoder
Solution- 3. Random Forest
Q:4. What is the limitation of deep learning?
1.Amount of data
2.Computational expensive
3.Data Labeling
4.All the above
Solution- 4. All the above
Q:5. The number of nodes in the hidden layer is 8 and the output layer is 5. The maximum number of connections from the hidden layer to the output layer are:
1.40
2.Less than 40
3.More than 40
4.It is an arbitrary value
Solution- 1. 40
Q:6. Recurrent Neural Networks (RNN) are used for
1.Businesses Help securities traders to generate analytic reports
2.Detecting fraudulent credit-card transaction
3.Providing a caption for images
4.All of the above
Solution- 4. All of the above
Q:7. Types of RNN are:
1.LSTM
2.Boltzman machine
3.Hopfield network
4.a and b
Solution- 4. a and b
Q:8. What is perceptron?
1.a single layer feed-forward neural network with pre-processing
2.an auto-associative neural network
3.a double layer auto-associative neural network
4.a neural network that contains feedb
Solution- 1. a single layer feed-forward neural network with pre-processing
Q:9. Which of the following architecture has feedback connections?
1.Recurrent Neural network
2.Convolutional Neural Network
3.Restricted Boltzmann Machine
4.None of these
Solution- 2.Convolutional Neural Network
Q:10. Bidirectional RNN:
1.Trained to predict both the positive and negative directions of time simultaneously.
2.Applications are speech recognition, handwritten recognition etc.
3.After forward and backward passes are done, the weights are updated
4.All the above
Solution- 4. All the above
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