deep learning for computer vision nptel assignment answers

Deep Learning For Computer Vision

Welcome to the course, welcome to the course #.

The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users. This course will introduce the students to traditional computer vision topics, before presenting deep learning methods for computer vision. The course will cover basics as well as recent advancements in these areas, which will help the student learn the basics as well as become proficient in applying these methods to real-world applications. The course assumes that the student has already completed a full course in machine learning, and some introduction to deep learning preferably, and will build on these topics focusing on computer vision.

Fall 2022 Link : https://onlinecourses.nptel.ac.in/noc22_cs76/preview

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

Instructor: Vineeth N Balasubramanian #

Vineeth N. Balasubramanian is an Associate Professor in the department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad (IIT-H), India. His research interests include deep learning, machine learning, and computer vision. His research has resulted in over 100 peer-reviewed publications at various international venues, including top-tier ones such as ICML, NeurIPS, CVPR, ICCV, KDD, AAAI, etc. His PhD dissertation at Arizona State University on the Conformal Predictions framework was nominated for the Outstanding PhD Dissertation at the Department of Computer Science. For more details, please visit his page, https://iith.ac.in/~vineethnb/

Teaching Assistants #

Rishabh Lalla (Research Assistant, Machine Learning and Vision Lab, IIT Hyderabad)

Charchit Sharma (Research Assistant, Machine Learning and Vision Lab, IIT Hyderabad)

Divyagna Bavikadi (Research Assistant, Machine Learning and Vision Lab, IIT Hyderabad)

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Modern Computer Vision

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

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NPTEL Deep Learning Assignment 1 Answers 2024

• by QuizXp Team
• January 21, 2024 January 21, 2024

Hello learners In this article we are going to discuss NPTEL Deep Learning Assignment 1 Answers . All the Answers provided below to help the students as a reference, You must submit your assignment with your own knowledge and use this article as reference only.

About the 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.

NPTEL Deep Learning Assignment 1 Answers 2024:

1. Which of the following is (are) region descriptor(s) ? Choose the correct option. 1) Fourier descriptor 1) co-occurrence matrix Ill) Intensity histogram IV) Signature

2. Consider a two class Bayes” Minimum Risk Classitier. Probability of class w1 1s P (w1) =0.4. P (x| wi1) = 0.65, P (x| w2) =0.5 and the loss matrix values are Find the RiskR (a1|x).

3. If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true?

4. Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficients and use only firstm (m<K) number of coefficients to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape?

5. Signature descriptor of an unknown shape is given in the figure, can you identify the unknown shape?

6. Signature descriptor of an unknown shape is given in the figure. If the value of k is 7 cm., what is the area of the unknown shape

7. Which or the Toliowing 1s not a Lo-occurrence matrix-basea aescriptors

8. Given an image | (fig 1), The gray co-occurrence matrix C (fig 2) can be constructed by specifying the displacement vector d = (dx, dy). Let the position operator be specified as (1, 1), which has the interpretation: one pixel to the right and one pixel below. (Both the image and the partial gray co-occurrence is given in the figure 1, and 2 respectively. Blank values and ‘X’,’Y’ values in gray co-occurrence matrix are unknown.)

9. What is the value of maximum probability descriptor?

10. The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as

Category: NPTEL

Programming in modern c++ | week 12, introduction to internet of things week 12, introduction to industry 4.0 and industrial internet of things | week 12, an introduction to artificial intelligence | week 12, the joy of computing using python | week 12, quantum mechanics 1 | week 12, programming in java | week 12, principles of management | week 12, leadership and team effectiveness | week 12, introduction to machine learning | week 12.

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2023

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2023:- In this post, We have provided answers of Deep Learning – IIT Ropar Assignment 4. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

NPTEL Deep Learning – IIT Ropar Week 4 Assignment Answer 2023

1. Which step does Nesterov accelerated gradient de s cent perform before finding the update size?

• Increase the momentum
• Estimate the next position of the parameters
• Adjust the learning rate
• Decrease the st e p size

2. Select the parameter of vanilla gradient descent controls the step size in the direction of the gradient.

• Learning rate
• None of t h e above

3. What does the distance between two contour lines on a contour map represent?

• The change in the output of function
• The direction of the function
• The rate of change of the function
• None of the a b ove

4. Which of the following represents the contour plot of the function f(x,y) = x2−y?

5. What is the main advantage of using Adagrad over other optimization algorithms?

• It converges faster than other optimization algorithms.
• It is less sensitive to the choice of h yperparameters (learning rate).
• It is more memory-efficient than other optimization algorithms.
• It is less likely to get stuck in local optima than other optimization algorithms.

6. We are training a neural network using the vanilla gradient descent algorithm. We observe that the change in weights is small in successive iterations. Wh a t are the possible causes for the following phenomenon?

• ∇w is s m all
• ∇w is large

7. You are given labeled data which we call X where rows are data points and columns feature. One column has most of its values as 0. What algorithm should we use here for fa s ter convergence and achieve the optimal value of the loss function?

• Stochastic gradient de s cent
• Momentum-based gradient descent

8. What is the update rule for the ADAM optimizer?

• wt=wt−1−lr∗(mt/(vt−− √ +ϵ))
• wt=wt−1−lr∗m
• wt=wt−1−lr∗(mt/(vt+ϵ))
• wt=wt−1−lr∗(vt/(mt+ϵ))

9. What is the advantage of using mini-batch gradient descent over batch gradient descent?

• Mini-batch gradient descent is more computationally efficient than batch gradient descent.
• Mini-batch gradient descent leads to a more accurate estimate of the gradient than batch gradient descent.
• Mini batch gradient descent gives us a better s o lution.
• Mini-batch gradient descent can converge faster than batch gradient descent.

10. Which of the following is a variant of gradient desc e nt that uses an estimate of the next gradient to update the current position of the parameters?

• Momentum optimization
• Stochastic gradient descent
• Nesterov accelerated gradi e nt descent

About Deep Learning IIT – Ropar

Deep Learning has received a lot of attention over the past few years and has been employed successfully by companies like Google, Microsoft, IBM, Facebook, Twitter etc. to solve a wide range of problems in Computer Vision and Natural Language Processing. In this course, we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. We will also look at various optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are used for training such deep neural networks. At the end of this course, students would have knowledge of deep architectures used for solving various Vision and NLP tasks 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.

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2022

1. Consider the movement on the 3D error surface for Vannila Gradient Descent Algorithm. Select all the options that are TRUE. a. Smaller the gradient, slower the movement b. Larger the gradient, faster the movement c. Gentle the slope, smaller the gradient d. Steeper the slope, smaller the gradient

2. Pick out the drawback in Vannila gradient descent algorithm. a. Very slow movement on gentle slopes b. Increased oscillations before converging c. escapes minima because of long strides d. Very slow movement on steep slopes

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3. Comment on the update at the t th  update in the Momentum-based Gradient Descent. a. weighted average of gradient b. Polynomial weighted average c. Exponential weighted average of gradient d. Average of recent three gradients

4. Given a horizontal slice of the error surface as shown in the figure below , if the error at the position p is 0.49 then what is the error at point q? a. 0.70 b. 0.69 c. 0.49 d. 0

5. Identify the update rule for Nesterov Accelerated Gradient Descent.

6. Select all the options that are TRUE for Line search. a. w is updated using different learning rates b. updated value of w always gives the minimum loss c. Involves minimum calculation d. Best value of Learning rate is used at every step

👇 For Week 05 Assignment Answers 👇

7. Assume you have 1,50,000 data points , Mini batch size being 25,000, one epoch implies one pass over the data, and one step means one update of the parameters, What is the number of steps in one epoch for Mini-Batch Gradient Descent? a. 1 b. 1,50,000 c. 6 d. 60

8. Which of the following learning rate methods need to tune two hyperparameters? I . step decay II. exponential decay III. 1/t decay a. I and II b. II and III c. I and III d. I, II and III

9. How can you reduce the oscillations and improve the stochastic estimates of the gradient that is estimated from one data point at a time? a. Mini-Batch b. Adam c. RMSprop d. Adagrad

10. Select all the statements that are TRUE. a. RMSprop is very aggressive when decaying the learning rate b. Adagrad decays the learning rate in proportion to the update history c. In Adagrad, frequent parameters will receive very large updates because of the decayed learning rate d. RMSprop has overcome the problem of Adagrad getting stuck when close to convergence

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3 thoughts on “NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2023”

Ans send cheyalee…bro

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