NPTEL Deep Learning Assignment 7 Answers 2023

Hello NPTEL Learners, In this article, you will find NPTEL Deep Learning Assignment 7 Week 7 Answers 2023. All the Answers are provided below to help the students as a reference don’t straight away look for the solutions, first try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

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NPTEL Deep Learning Assignment 7 Answers 2023
NPTEL Deep Learning Assignment 7 Answers 2023

NPTEL Deep Learning Assignment 7 Answers 2023:

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Q.1. Select the correct option about Autoencoder.

Statement 1: Autoencoder can be used for image compression. Statement 2: Autoencoder can be used for unsupervised pre-training for image classification.

  • a. Both statements are true
  • b. Statement 1 is true, but Statement 2 is false
  • c. Statement 1 is false, but statement 2 is true
  • d. Both statements are false

Q.2. What is not a purpose of the stacked autoencoder?

  • a. Memory Efficient Training,
  • b. Better Convergence
  • c. Faster inference
  • d. All of the above is the purpose of using stacked autoencoder

Q.3. Which autoencoder is the most effective for the dimensionality reduction of the data?

  • a. Overcomplete Denoising Autoencoder
  • b. Overcomplete Stacked Autoencoder
  • c. Undercomplete Denoising Autoencoder
  • d. Undercomplete Stacked Autoencoder
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NPTEL Deep Learning Assignment 7 Answers 2023

Q.4. An overcomplete autoencoder generally learns identity function. How can we prevent those autoencoder from learning the identity function and learn some useful representations?

  • a. Stack autoencoder based layer-wise training
  • b. Train the autoencoder for large number of epochs in order to learn more useful representation
  • c. Add noise to the data and train to learn noise-free data from noisy data
  • d. It is not possible to train overcomplete autoencoder. It always converges to the identity function.

Q.5. In which conditions, autoencoder has more powerful generalization than Principal Components Analysis (PCA) while performing dimensionality reduction?

  • a. Undercomplete Linear Autoencoder
  • b. Overcomplete Linear Autoencoder
  • c. Undercomplete Non-linear Autoencoder
  • d. Overcomplete Non-Linear Autoencoder

Q.6. A autoencoder consists of 100 input neurons, 50 hidden neurons. If the network weights are represented using single precision floating point numbers then what will be size of weight matrix?

  • a. 10,000 Bytes
  • b. 10,150 Bits
  • c. 40,000 Bytes
  • d. 40,600 Bytes

Q.7. Which of the following is not the purpose of cost function in training denoising autoencoders?

  • a. Dimension reduction
  • b. Error minimization
  • c. Weight Regularization
  • d. Image denoising
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Q.8. What is the role of sparsity constraint in a sparse autoencoder?

  • a. Control the number of active nodes in a hidden layer
  • b. Control the noise level in a hidden layer
  • c. Control the hidden layer length
  • d. Not related to sparse autoencoder

Q.9. Which of the following autoencoder is not a regularization autoencoder?

  • a. Sparse autoencoder
  • b. Denoising autoencoder
  • c. Both a and b
  • d. Stack autoencoder

Q.10. Which of the following is NOT an application of an autoencoder?

  • a. Dimensionality reduction
  • b. Feature learning
  • c. Image compression
  • d. Image segmentation.
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NPTEL Deep Learning Assignment 7 Answers 2023

Disclaimer: This answer is provided by us only for discussion purpose if any answer will be getting wrong don’t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided by Chase2learn. This tutorial is only for Discussion and Learning purpose.

About NPTEL Deep Learning Course:

The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.

Course Layout:

  • Week 1:  Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
  • Week 2:  Linear Classifiers, Linear Machines with Hinge Loss
  • Week 3:  Optimization Techniques, Gradient Descent, Batch Optimization
  • Week 4:  Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
  • Week 5:  Unsupervised Learning with Deep Network, Autoencoders
  • Week 6:  Convolutional Neural Network, Building blocks of CNN, Transfer Learning
  • Week 7:  Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
  • Week 8:  Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
  • Week 9:  Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.
  • Week 10: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.
  • Week 11: LSTM Networks
  • Week 12: Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

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