Deep Learning IIT Ropar Week 6 Nptel Answers
Are you looking for the Deep Learning IIT Ropar Week 6 NPTEL Assignment Answers 2024 (July-Dec)? You’ve come to the right place! Access the most accurate and up-to-date solutions for your Week 6 assignment in the Deep Learning course offered by IIT Ropar.
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Deep Learning IIT Ropar Week 6 Nptel Assignment Answers (July-Dec 2024)
1. We are given an autoencoder A. The average activation value of neurons in this network is 0.01. The given autoencoder is:
A) Contractive autoencoder B) Overcomplete neural network C) Denoising autoencoder D) Sparse autoencoder
Answer: D) Sparse autoencoder
2. If an under-complete autoencoder has an input layer with a dimension of 7, what could be the possible dimension of the hidden layer?
A) 6 B) 8 C) 0 D) 7 E) 2
Answer: C) 0 E) 2
3. What is the primary reason for adding corruption to the input data in a denoising autoencoder?
A) To increase the complexity of the model. B) To improve the model’s ability to generalize to unseen data. C) To reduce the size of the training dataset. D) To increase the training time.
Answer: Updating soon in Progress
4. Suppose for one data point we have features ( x1, x2, x3, x4, x5 ) as −3, 7, 2.1, 0, 12.5 then, which of the following function should we use on the output layer (decoder)?
A) Logistic B) Linear C) ReLU D) Tanh
Answer: B) Linear
These are Deep Learning IIT Ropar Week 6 Nptel Assignment Answers
5. What is/are the primary advantages of Autoencoders over PCA?
A) Autoencoders are less prone to overfitting than PCA. B) Autoencoders are faster and more efficient than PCA. C) Autoencoders can capture nonlinear relationships in the input data. D) Autoencoders require fewer input data than PCA.
Answer: C) Autoencoders can capture nonlinear relationships in the input data.
6. What type of autoencoder is it when the hidden layer’s dimensionality is less than that of the input layer?
A) Under-complete autoencoder B) Complete autoencoder C) Overcomplete autoencoder D) Sparse autoencoder
Answer: C) Overcomplete autoencoder
7. Which of the following statements about overfitting in overcomplete autoencoders is true?
A) Reconstruction error is very low while training B) Reconstruction error is very high while training C) Network fails to learn good representations of input D) Network learns good representations of input
8. Which of the following statements about regularization in autoencoders is always true?
A) Regularisation reduces the search space of weights for the network. B) Regularisation helps to reduce the overfitting in overcomplete autoencoders. C) Regularisation shrinks the size of weight vectors learned. D) All of these.
9. We are using the following autoencoder with linear encoder and linear decoder. The eigenvectors associated with the covariance matrix of our data ( X ) are ( (V1, V2, V3, V4, V5) ). What are the representations most likely to be learned by our hidden layer ( H )? (Eigenvectors are written in decreasing order to the eigenvalues associated with them)
A) ( V1, V2 ) B) ( V4, V5 ) C) ( V1, V3 ) D) ( V1, V2, V3, V4, V5 )
Answer: A) ( V1, V2 )
10. What is the primary objective of sparse autoencoders that distinguishes it from vanilla autoencoder?
A) They learn a low-dimensional representation of the input data B) They minimize the reconstruction error between the input and the output C) They capture only the important variations/features in the data D) They maximize the mutual information between the input and the output
Answer: C) They capture only the important variations/features in the data
Check here all Deep Learning IIT Ropar Nptel Assignment Answers : Click here
For answers to additional Nptel courses, please refer to this link: NPTEL Assignment Answers
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NPTEL Deep Learning – IIT Ropar Week 8 Assignment Answer 2023
NPTEL Deep Learning – IIT Ropar Week 8 Assignment Solutions
1. Which of the following best describes the concept of saturation in deep learning?
- When the activation function output approaches either 0 or 1 and the gradient is close to zero.
- When the activation function output is very small and the gradient is close to zero.
- When th e activation function output is very large and the gradient is close to zero.
- None of the above.
2. Which of the following methods can help to avoid saturation in deep learning?
- Using a different activation function.
- Increasing t h e learning rate.
- Increasing the model complexity
- All of the above.
3. Which of the following is true about the role of unsupervised pre-training in deep learning?
- It is used to replace the need for labeled data
- It is used to initialize t h e weights of a deep neural network
- It is used to fine-tune a pre-trained model
- It is only useful for small datasets
4. Which of the following is an advantage of unsupervised pre-training in deep learning?
- It helps in reducing overfitting
- Pre-trained models converge faster
- It improv e s the accuracy of the model
- It requires fewer computational resources
5. What is the main cause of the Dead ReLU problem in deep learning?
- High variance
- High ne g ative bias
- Overfitting
- Underfitting
6. How can you tell if your network is suffering from the Dead ReLU problem?
- The loss function is not decreasing during training
- The accuracy of the network is not improving
- A large number of neurons ha v e zero output
- The network is overfitting to the training data
7. What is the mathematical expression for the ReLU activation function?
- f(x) = x if x < 0, 0 otherwise
- f(x) = 0 if x > 0, x otherwise
- f(x) = max(0,x)
- f(x) = min(0,x)
8. What is the main cause of the symmetry breaking problem in deep learning?
- Equal initializat io n of weights
9. What is the purpose of Batch Normalization in Deep Learning?
- To improve the generalization of the model
- To reduce overfitting
- To reduce bias in th e model
- To ensure that the distribution of the inputs at different layers doesn’t change
10. In Batch Normalization, which parameter is learned during training?
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Official Repo for Deep Learning for Compyter Vision Course offered by NPTEL
DL4CV-NPTEL/Deep-Learning-For-Computer-Vision
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Deep-learning-for-computer-vision, fall 2022 link : https://onlinecourses.nptel.ac.in/noc22_cs76/preview, lectures: https://www.youtube.com/watchv=rfavjcf1_zi&list=plyqspqzte6m_pi-riz4o1jegffhju9ggg, course cirriculum, week 1:introduction and overview:.
Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution
Week 2:Visual Features and Representations:
Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG, LBP, etc.
Week 3:Visual Matching:
Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow
Week 4:Deep Learning Review:
Review of Deep Learning, Multi-layer Perceptrons, Backpropagation
Week 5:Convolutional Neural Networks (CNNs):
Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG, InceptionNets, ResNets, DenseNets
Week 6:Visualization and Understanding CNNs:
Visualization of Kernels; Backprop-to-image/Deconvolution Methods; Deep Dream, Hallucination, Neural Style Transfer; CAM,Grad-CAM, Grad-CAM++; Recent Methods (IG, Segment-IG, SmoothGrad)
Week 7:CNNs for Recognition, Verification, Detection, Segmentation:
CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet; CNNs for Segmentation: FCN, SegNet, U-Net, Mask-RCNN
Week 8:Recurrent Neural Networks (RNNs):
Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition
Week 9:Attention Models:
Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks
Week 10:Deep Generative Models:
Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etc
Week 11:Variants and Applications of Generative Models in Vision:
Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security; Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etc
Week 12:Recent Trends:
Zero-shot, One-shot, Few-shot Learning; Self-supervised Learning; Reinforcement Learning in Vision; Other Recent Topics and Applications
Contributors 3
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Machine Learning and Deep Learning - Fundamentals and Applications
| Course Status : | Ongoing |
| Course Type : | Core |
| Duration : | 12 weeks |
| Category : | |
| Credit Points : | 3 | Undergraduate/Postgraduate |
| Start Date : | 22 Jul 2024 |
| End Date : | 11 Oct 2024 |
| Enrollment Ends : | 05 Aug 2024 |
| Exam Registration Ends : | 16 Aug 2024 |
| Exam Date : | 03 Nov 2024 IST |
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