NPTEL Edge Computing Week 4 Assignment Answers 2024

Join Our WhatsApp Group Join Now
Join Us On Telegram Join Now

NPTEL Edge Computing Week 4 Assignment Answers 2024

1. Singular Value Decomposition is an example of which model compression technique?

  • Parameter pruning
  • Low rank factorization
  • Compact convolutional filters
  • Knowledge distillation
Answer :- For Answer Click Here 

2. In vanilla knowledge distillation, knowledge is transferred from the teacher model to the student model by minimizing a loss function in which the target is the distribution of class probabilities predicted by the ___________

  • distilled model
  • student model
  • teacher model
  • none of the above
Answer :- For Answer Click Here 

3. Which of the following is used in knowledge distillation to smooth-out or soften the target probabilities?

  • Temperature
  • KD-Loss
  • KL-Divergence
  • Cross-Entropy
Answer :- For Answer Click Here 

4. In a __________ knowledge distillation, the output of intermediate layers can be used as “knowledge” to supervise the training of the student model.

  • Layer-Based
  • Relation-Based
  • Response-Based
  • Feature-Based
Answer :- 

5. In online-distillation, the knowledge is transferred from a pre-trained model into a student model instead of training the teacher model simultaneously with the student. The statement is,

  • True
  • False
Answer :- 

6. Federated Learning (FL) aims to train a ML model in a centralized manner while keeping the data decentralized. The statement is,

  • True
  • False
Answer :- For Answer Click Here 

7. Choose the incorrect statements in context of federated averaging (FedAvg) algorithm,

  • In each round of FedAvg, server performs aggregation before clients train their local models using local data-points.
  • In each round of FedAvg, aggregation is performed over model parameters submitted only by the participating clients.
  • The aggregation server is responsible to initialize the model at the beginning of FedAvg algorithm.
  • A Client sends its copy of the model to every other client at the end of each round.
Answer :- 

8. When local datasets are _, FedAvg suffers from client drift.

  • non-I.I.D.
  • very large in size
  • identical to each other
  • independent of each other
Answer :- 

9. Select the scenarios where the use of Federated learning is justifiable.

  • Data privacy is needed
  • Clients have no connectivity to cloud or edge
  • Cost of data transfer is very high
  • Performance of an existing model is at the ceiling
Answer :- 

10. Representation Learning is a subset of __________.

  • Deep Learning
  • Machine Learning
  • Artificial Intelligence
  • Knowledge Distillation
Answer :- For Answer Click Here