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✅ Subject: Deep Learning – IIT Ropar
📅 Week: 6
🎯 Session: NPTEL 2025
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NPTEL Deep Learning – IIT Ropar Week 6 Assignment Answers 2025
1. 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.
Answer :- For Answers Click Here
2. Which of the following is a potential advantage of using an overcomplete autoencoder?
- Reduction of the risk of overfitting
- Faster training time
- Ability to learn more complex and nonlinear representations
- To compress the input data
Answer :-
3. We are given an autoencoder A. The average activation value of neurons in this network is 0.015. The given autoencoder is
- Contractive autoencoder
- Sparse autoencoder
- Overcomplete neural network
- Denoising autoencoder
Answer :- For Answers Click Here
4. Suppose we build a neural network for a 5-class classification task. Suppose for a single training example, the true label is [0 1 0 0 1] while the predictions by the neural network are [0.4 0.25 0.2 0.1 0.6]. What would be the value of cross-entropy loss for this example? (Answer up to two decimal places, Use base 2 for log-related calculations)
Answer :-
5. If an under-complete autoencoder has an input layer with a dimension of 5, what could be the possible dimension of the hidden layer?
- 5
- 4
- 2
- 0
- 6
Answer :-
6. Which of the following networks represents an autoencoder?
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7. What is the primary reason for adding corruption to the input data in a denoising autoencoder?
- To increase the complexity of the model.
- To improve the model’s ability to generalize to unseen data.
- To reduce the size of the training dataset.
- To increase the training time.
Answer :-
8. Suppose for one data point we have features x1,x2,x3,x4,x5 as −4,6,2.8,0,17.3 then, which of the following function should we use on the output layer(decoder)?
- Linear
- Logistic
- Relu
- Tanh
Answer :-
9. Which of the following statements about overfitting in overcomplete autoencoders is true?
- Reconstruction error is very high while training
- Reconstruction error is very low while training
- Network fails to learn good representations of input
- Network learns good representations of input
Answer :-
10. What is the purpose of a decoder in an autoencoder?
- To reconstruct the input data
- To generate new data
- To compress the input data
- To extract features from the input data
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Deep Learning – IIT Ropar Week 6 NPTEL Assignment Answers 2024
1. Which of the following networks represents an autoencoder?
Answer :- b
2. If an under-complete autoencoder has an input layer with a dimension of 7, what could be the possible dimension of the hidden layer?
- 6
- 8
- 0
- 7
- 2
Answer :- a, e
3. What type of autoencoder is it when the hidden layer’s dimensionality is less than that of the input layer?
- Under-complete autoencoder
- Complete autoencoder
- Overcomplete autoencoder
- Sparse autoencoder
Answer :- a
4. Which of the following statements about regularization in autoencoders is always true?
- Regularisation reduces the search space of weights for the network.
- Regularisation helps to reduce the overfitting in overcomplete autoencoders.
- Regularisation shrinks the size of weight vectors learned.
- All of these.
Answer :- a, b
5. We are using the following autoencoder with linear encoder and linear decoder. The eigenvectors associated with the covariance matrix of our data X is (V1,V2,V3,V4,V5). What are the representations most likely to be learned by our hidden layer H? (Eigenvectors are written in decreasing order to the eigenvalues associated with them)
- V1,V2
- V4,V5
- V1,V3
- V1,V2,V3,V4,V5
Answer :- a
6. What is the purpose of a decoder in an autoencoder?
- To reconstruct the input data
- To generate new data
- To compress the input data
- To extract features from the input data
Answer :- a
7. What are the advantages of using a denoising autoencoder?
- Robustness to noisy input data
- Reduction of the risk of overfitting
- Faster training time
- It promotes sparsity in the hidden layer
Answer :- a, b
8. We are given an autoencoder A. The average activation value of neurons in this network is 0.06. The given autoencoder is:
- Contractive autoencoder
- Overcomplete neural network
- Sparse autoencoder
- Denoising autoencoder
Answer :- c
9. What are the possible applications of autoencoders?
- Data Compression
- Extraction of important features
- Reducing noise
- All of these
Answer :- d
10. 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.
Answer :- a, c
Conclusion:
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