### Artificial Intelligence For Engineering Assignment 8 | 1st year | aktu by Professor

uploaded:: 11:53:40am 2 Jun 2021 ### Artificial Intelligence For Engineering Assignment 7 | 1st year | aktu

Q:1. The incorrect statement for a Convolutional Neural Network are:

1.The height and width of the filter in CNN must be less than the size of input

2.The Pooling layer progressively increases the spatial size of the representation

3.It uses both linear and non-linear activation functions

4.The last few layers are fully connected layers and computation on these layers are very time consuming

Solution- 1.The height and width of the filter in CNN must be less than the size of input

Q:2. A Convolutional Neural Network is able to successfully capture the Spatial and Temporal dependencies:

1.True

2.False

Solution- 1.True

Q:3. Different types of normalization in Deep Neural Networks are

a. Output
b. Batch
c. Group
d. Instance

1.a,b,c

2.b,c,d

3.d,a,b

4.d,a,c

Solution- 2.b,c,d

Q:4. Applications of CNNs are:

a. Recommender systems
b. AlexNet
c. Natural Language Processing
d. Pooling

1.a,b

2.b,d

3.a,c

4.a,d

Solution- 4.a,d

Q:5. Which of the following statements are correct for GAN?

a. GANs are useful for unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning
b. Generative model technique learns to generate the data with the same statistics of training data
c. At each iteration the goal of generator is to minimize the classification error and the goal of discriminator is to maximize the classification error.
d. The discriminator could tell the difference between images of a cat and a dog and generative model could generate new images of animals that look like real animals.

1.a,b,c

2.a,b,d

3.a,c,d

4.b,c,d

Solution- 2.a,b,d

Q:6. A generative model:

a. Captures the joint probability p(X,Y)
b. Captures the conditional probability p(Y|X)
c. Includes the distribution of data itself
d. Cannot predict the next word in sequence

1.a,b

2.a,c

3.a,d

4.b,c

Solution- 2. a, c

Q:7. The discriminative model:

a. Draw boundaries in the data space as it tells the difference between handwritten 0s and 1s.
b. Captures the joint probability p(X,Y)
c. Captures the conditional probability p(Y|X)
d. Learns to distinguish the generator’s fake data from real data

1.a,b,c

2.a,b,d

3.a,c,d

4.b,c,d

Solution- 3. a,c,d

Q:8. Choose the incorrect statements from the following

1.The discriminator uses the real data as negative examples during training

2.The discriminator uses the fake data as negative examples during training

3.The portion of the GAN that trains the generator model includes random input

4.None of the above

Solution- 1.The discriminator uses the real data as negative examples during training

Q:9. Choose the correct statements from the following

a. Most universal approximation theorems can be parsed into two classes. The first quantifies the approximation capabilities of neural networks with an arbitrary number of artificial neurons and the second quantifies an arbitrary number of hidden layers
b. A neural network can represent any function provided it has sufficient capacity.
c. Not all architectures can represent any function
d. None of the above

1.a,b,c

2.b,c,d

3.d,a,b

4.d,a,c

Solution- 1.a,b,c

Q:10. Interesting applications of Generative Adversarial Networks (GANs) are:

1.Photo Inpainting

2.Culinary arts (as making a pizza)

3.Face aging

4.All the above

Solution- 4. All the above

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