Hello NPTEL Learners, In this article, you will find NPTEL Deep Learning Assignment 8 Week 8 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 8 Answers 2023:
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Q.1. Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes such that the sum of p over all n equals to 1?
Q.2. The input image has been converted into a matrix of size 256 X 256 and a kernel/filter of size 5Ă—5 with a stride of 1 and no padding. What will be the size of the convoluted matrix?
- a. 252Ă—252
- b. 3Ă—3
- c. 254Ă—254
- d. 256Ă—256
Q.3. What will be the range of output if we apply ReLU non-linearity and then Sigmoid Nonlinearity subsequently after a convolution layer?
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Q.4. The figure below shows image of a face which is input to a convolutional neural net and the other three images shows different levels of features extracted from the network. Can you identify from the following options which one is correct?
- a. Label 3: Low-level features, Label 2: High-level features, Label 1: Mid-level features
- b. Label 1: Low-level features, Label 3: High-level features, Label 2: Mid-level features
- c. Label 2: Low-level features, Label 1: High-level features, Label 3: Mid-level features
- d. Label 3: Low-level features, Label 1: High-level features, Label 2: Mid-level features
Q.5. Suppose you have 8 convolutional kernel of size 5 x 5 with no padding and stride 1 in the first layer of a convolutional neural network. You pass an input of dimension 228 x 228 x 3 through athis layer. What are the dimensions of the data which the next layer will receive?
Q.6. What is the mathematical form of the Leaky RelU layer?
- a. f(x)=max(0,x)
- b. f(x)=min(0,x)
- c. f(x)=min(0, ax), where a is a small constant
- d. f(x)=1(x<0)(ax)+1(x>=0)(x), where a is a small constant
Q.7. The input image has been converted into a matrix of size 224 x 224 and convolved with a kernel/filter of size FxF with a stride of s and padding P to produce a feature map of dimension 222Ă—222. Which among the following is true?
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Q.8. Statement 1: For a transfer learning task, lower layers are more generally transferred to another task Statement 2: For a transfer learning task, last few layers are more generally transferred to another task Which of the following option is correct?
- a. Statement 1 is correct and Statement 2 is incorrect
- b. Statement 1 is incorrect and Statement 2 is correct
- c. Both Statement 1 and Statement 2 are correct
- d. Both Statement 1 and Statement 2 are incorrect
Q.9. Statement 1: Adding more hidden layers will solve the vanishing gradient problem for a 2-layer neural network Statement 2: Making the network deeper will increase the chance of vanishing gradients.
- a. Statement 1 is correct
- b. Statement 2 is correct
- c. Neither Statement 1 nor Statement 2 is correct
- d. Vanishing gradient problem is independent of number of hidden layers of the neural network.
Q.10. How many convolution layers are there in a LeNet-5 architecture?
- a. 2
- b. 3
- c. 4
- d. 5
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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:Â Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
- Week 2:Â Linear Classifiers, Linear Machines with Hinge Loss
- Week 3:Â Optimization Techniques, Gradient Descent, Batch Optimization
- Week 4:Â Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
- Week 5:Â Â Unsupervised Learning with Deep Network, Autoencoders
- Week 6:Â Convolutional Neural Network, Building blocks of CNN, Transfer Learning
- Week 7:Â Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
- Week 8:Â Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
- Week 9:Â 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:Â LSTM Networks
- Week 12:Â 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.
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