Hello Learners, In this Post, you will find **NPTEL Deep Learning Assignment 6 Week 6 Answers 2023**. All the Answers are provided below to help the students as a reference donāt straight away look for the solutions.

**NPTEL Deep Learning Assignment 7 Answers Join Groupš **

**Note:** First try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

COURSE NAME | ANSWER |

NPTEL Deep Learning Assignment 1 Answers | Click Here |

NPTEL Deep Learning Assignment 2 Answers | Click Here |

NPTEL Deep Learning Assignment 3 Answers | Click Here |

NPTEL Deep Learning Assignment 4 Answers | Click Here |

NPTEL Deep Learning Assignment 5 Answers | Click Here |

NPTEL Deep Learning Assignment 6 Answers | Click Here |

NPTEL Deep Learning Assignment 7 Answers | Click Here |

NPTEL Deep Learning Assignment 8 Answers | Click Here |

NPTEL Deep Learning Assignment 9 Answers | Click Here |

NPTEL Deep Learning Assignment 10 Answers | Click Here |

NPTEL Deep Learning Assignment 11 Answers | Click Here |

NPTEL Deep Learning Assignment 12 Answers | Click Here |

## NPTEL Deep Learning Assignment 6 Answers 2023:

**We are updating answers soon Join Group for update: CLICK HERE**

#### Q.1. Which of the following is considered for correcting a weight during back propagation?

- a. Positive gradient of weight
- b. Gradient of error
- c. Negative gradient of error w.r.t weight
- d. Negative gradient of weight

#### Q.2. What will happen when learning rate is set to zero?

- a. Weight update will be very slow
- b. Weights will be zero
- c. Weight update will tend to zero but not exactly zero
- d. Weights will not be updated

#### Q.3. During back-propagation through max pooling with stride the gradients are

- a. Evenly distributed
- b. Sparse gradients are generated with non-zero gradient at the max response location
- c. Differentiated with respect to responses
- d. None of the above

**NPTEL Deep Learning Assignment 7 Answers Join Groupš **

#### Q.4. Gradient of sigmoid function is maximum at x=?

- a. 0
- b. Positive Infinity
- c. Negative Infinity
- d. 1

#### Q.5. The derivative of the loss function with respect to the weights in a deep neural network can be computed as,

- a. Sum of derivative of cost function, derivative of non-linear transfer function and derivative of linear network.
- b. Product of derivative of cost function and derivative of non-linear transfer function.
- c. Product of derivative of cost function, derivative of non-linear transfer function and derivative of linear network.
- d. Sum of derivative of cost function and derivative of non-linear transfer function.

#### Q.6. Which of the following models can be employed for unsupervised learning?

- a. Autoencoder
- b. Restricted Boltzmann machines
- c. Bothaandb
- d. None

#### Q.7. Find the gradient component āgā of this function.

- a. 2
- b. e2
- c. 2e2
- d. 4

**NPTEL Deep Learning Week 6 Answers Join Groupš **

**CLICK HERE**

#### Q.8. What is the similarity between an autoencoder and Principle Component Analysis (PCA)?

- a. Both assume nonlinear systems
- b. Subspace of weight matrices
- c. Both can assume linear systems
- d. All of these

#### Q.9. Which of the following is only an unsupervised learning problem?

- a. Digit Recognition
- b. Image Segmentation
- c. Image Compression
- d. All of the above

#### Q.10. What is the dimension of encoder weight matrix of an autoencoder (hidden units=400) constructed to handle 10-dimensional input samples?

- a. rows =10 and columns = 401
- b. rows =400 and columns = 10
- c. rows =11 and columns = 400
- d. rows =400 and columns = 11

**NPTEL Deep Learning Assignment 6 Answers Join Groupš **

**Disclaimer**: This answer is provided by us only for discussion purpose if any answer will be getting wrong donāt blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided byĀ **Brokenprogrammers**. This tutorial is only for Discussion andĀ LearningĀ 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**:Ā Introduction to Deep Learning, Bayesian Learning, Decision Surfaces**Week 2:**Ā Linear Classifiers, Linear Machines with Hinge Loss**Week 3:**Ā Optimization Techniques, Gradient Descent, Batch Optimization**Week 4:**Ā Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning**Week 5:Ā**Ā Unsupervised Learning with Deep Network, Autoencoders**Week 6:**Ā Convolutional Neural Network, Building blocks of CNN, Transfer Learning**Week 7:**Ā Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam**Week 8:**Ā Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization**Week 9:**Ā 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:**Ā LSTM Networks**Week 12:**Ā Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

**CRITERIA TO GET A CERTIFICATE**:

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.**

If you have not registered for exam kindly register Through https://examform.nptel.ac.in/