NPTEL Deep Learning – IIT Ropar Week 6 Assignment Answer 2023

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NPTEL Deep Learning – IIT Ropar Week 6 Assignment Solutions

NPTEL Deep Learning - IIT Ropar Assignment Answers
NPTEL Deep Learning – IIT Ropar Assignment Answer 2023

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?

NPTEL Deep Learning - IIT Ropar Week 6 Assignment Answer 2023
NPTEL Deep Learning - IIT Ropar Week 6 Assignment Answer 2023

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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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Course NameDeep Learning – IIT Ropar
CategoryNPTEL Assignment Answer
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