NPTEL Deep Learning – IIT Ropar Week 6 Assignment Solutions

## NPTEL Deep Learning – IIT Ropar Week 6 Assignment Answer 2023

**1. What is the main purpose of a hidden layer in an under-complete autoencoder?**

- To increase the number of neurons in the network
- To reduce the number of neurons in the network
- To limit the capacity of the network
- None of These

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**2. Which of the following problems prevents us from using autoencoders for the task of Image compression?**

- Images are not allowed as input to autoencoders
- Difficulty in training deep neural networks
- Loss of image quality due to compression
- Auto encoders are not capable of producing image output

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**3. Which of the following is a potential advantage of using an overcomplete autoencoder?**

- Reduction of the risk of overfitting
- Ability to learn more complex and nonlinear representations
- Faster training time
- To compress the input data

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**4. What is/are the primary advantages of Autoencoders over PCA?**

- Autoencoders are less prone to overfitting than PCA.
- Autoencoders are faster and more efficient than PCA.
- Autoencoders require fewer input data than PCA.
- Autoencoders can capture nonlinear relationships in the input data.

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**5. Which of the following is a potential disadvantage of using autoencoders for dimensionality reduction over PCA?**

- Autoencoders are computationally expensive and may require more training data than PCA.
- Autoencoders are bad at capturing complex relationships in data
- Autoencoders may overfit the training data and generalize poorly to new data.
- Autoencoders are unable to handle linear relationships between data.

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**6. What is the primary objective of sparse autoencoders that distinguishes it from vanilla autoencoder?**

- They learn a low-dimensional representation of the input data
- They minimize the reconstruction error between the input and the output
- They capture only the important variations/features in the data
- They maximize the mutual information between the input and the output

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**7. Which of the following networks represents an autoencoder?**

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**8. If the dimension of the hidden layer representation is more than the dimension of the input layer, then what kind of autoencoder do we have?**

- Complete autoencoder
- Under-complete autoencoder
- Overcomplete autoencoder
- Sparse autoencoder

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**9. Suppose for one data point we have features x1,x2,x3,x4,x5 as −2,12,4.2,7.6,0 then, which of the following function should we use on the output layer(decoder)?**

- Logistic
- Relu
- Tanh
- Linear

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**10. If the dimension of the input layer in an under-complete autoencoder is 6, what is the possible dimension of the hidden layer?**

- 6
- 2
- 8
- 0

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Category | NPTEL Assignment Answer |

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