nptel deep learning iit ropar assignment answers week 1

[Week 1] NPTEL Deep Learning – IIT Ropar Assignment Answers 2024

Nptel deep learning – iit ropar week 1 assignment answers 2024.

1. Which Boolean function with two inputs x1 and x2 is represented by the following decision boundary? (Points on boundary or right of the decision boundary to be classified 1)

2. Choose the correct input-output pair for the given MP Neuron.

3. Suppose we have a boolean function that takes 4 inputs x1, x2, x3, x4? We have an MP neuron with parameter θ=2. For how many inputs will this MP neuron give output y=1?

4. We are given the following data:

• None of These

6. Consider points shown in the picture. The vector w is (-1,0). As per this weight vector, the Perceptron algorithm will predict which classes for the data points x1 and x2.

7. Given an MP neuron with the inputs as x1,x2,x3,x4,x5 and threshold θ=3 where x5 is inhibitory input. For input (1,1,1,0,1) what will be the value of y?

• y=1 since θ≥3
• Insufficient information

8. An MP neuron takes two inputs x1 and x2. Its threshold is θ=0. Select all the boolean functions this MP neuron may represent.

10. What is the ”winter of AI” referring to in the history of artificial intelligence?

• The period during winter when AI technologies are least effective due to cold temperatures
• A phase marked by decreased funding and interest in AI research.
• The season when AI algorithms perform at their peak efficiency.
• A period characterized by rapid advancements and breakthroughs in AI technologies.

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Deep Learning _ Part 1(IIT Ropar)

₹ 3,000.00

Prof. Sudarshan Iyengar Prof. Sanatan Sukhija IIT Ropar , Mahindra University, Hyderabad

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Additional information, certification process, course details.

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INTENDED AUDIENCE

Prerequisites, about the instructor.

Prof. Sudarshan Iyengar, Associate Professor at the CSE at IIT Ropar has a Ph.D. from the Indian Institute of Science (IISc). An exemplary teacher who has delivered over 350 popular science talks to students of high school and advanced graduate programmes. Dr. Sudarshan has offered more than 100 hours of online lectures with novel teaching methodologies that have reached lakhs of Students. His research interests include Data Sciences, Social Computing, Social Networks, Collective Intelligence, Crowdsourced Technologies and Secure Computation

Prof. Sanatan Sukhija is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Mahindra University, Hyderabad. He earned his Doctorate from the Department of Computer Science and Engineering at Indian Institute of Technology Ropar. Prior to joining Mahindra University, his varied career includes stints at several industries and academic institutions, including, Amazon, Intel, Siemens, HCL, Punjab Engineering College, and the NorthCap University. He works on specific machine learning problems, in particular, transfer learning and domain adaptation. This research area focuses on learning in those domains where the amount of labeled training data is scarce or absent. His other research deals with learning robust deep models for industry/healthcare related problems. His work has led to multiple publications at several top-tier venues (AI Journal, AAAI, IJCAI, ECML-PKDD, IJCNN, WCCI etc.)

1. Join the course Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE Assignment score = Score more than 50% in at least 9/12 assignments. Exam score = 50% of the proctored certification exam score out of 100 Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

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5. Results and Certification After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

Week 1  :  (Partial) History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm Week 2  :  Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks Week 3  :  FeedForward Neural Networks, Backpropagation Week 4  :  Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis Week 5  :  Principal Component Analysis and its interpretations, Singular Value Decomposition Week 6  :  Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders Week 7  :  Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout Week 8  :  Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization Week 9  :  Learning Vectorial Representations Of Words Week 10 : Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks Week 11 : Recurrent Neural Networks, Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs Week 12 : Encoder Decoder Models, Attention Mechanism, Attention over images

Books and References:

Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville http://www.deeplearningbook.org

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• Computer Science and Engineering
• NOC:Deep Learning- Part 1 (Video) 
• Co-ordinated by : IIT Ropar
• Available from : 2018-04-25
• Intro Video
• Biological Neuron
• From Spring to Winter of AI
• The Deep Revival
• From Cats to Convolutional Neural Networks
• Faster, higher, stronger
• The Curious Case of Sequences
• Beating humans at their own games (literally)
• The Madness (2013-)
• (Need for) Sanity
• Motivation from Biological Neurons
• McCulloch Pitts Neuron, Thresholding Logic
• Perceptrons
• Error and Error Surfaces
• Perceptron Learning Algorithm
• Proof of Convergence of Perceptron Learning Algorithm
• Deep Learning(CS7015): Linearly Separable Boolean Functions
• Deep Learning(CS7015): Representation Power of a Network of Perceptrons
• Deep Learning(CS7015): Sigmoid Neuron
• Deep Learning(CS7015): A typical Supervised Machine Learning Setup
• Deep Learning(CS7015): Learning Parameters: (Infeasible) guess work
• Deep Learning(CS7015): Learning Parameters: Gradient Descent
• Deep Learning(CS7015): Representation Power of Multilayer Network of Sigmoid Neurons
• Feedforward Neural Networks (a.k.a multilayered network of neurons)
• Learning Paramters of Feedforward Neural Networks (Intuition)
• Output functions and Loss functions
• Backpropagation (Intuition)
• Backpropagation: Computing Gradients w.r.t. the Output Units
• Backpropagation: Computing Gradients w.r.t. Hidden Units
• Backpropagation: Computing Gradients w.r.t. Parameters
• Backpropagation: Pseudo code
• Derivative of the activation function
• Information content, Entropy & cross entropy
• Recap: Learning Parameters: Guess Work, Gradient Descent
• Contours Maps
• Momentum based Gradient Descent
• Nesterov Accelerated Gradient Descent
• Stochastic And Mini-Batch Gradient Descent
• Tips for Adjusting Learning Rate and Momentum
• Line Search
• Gradient Descent with Adaptive Learning Rate
• Bias Correction in Adam
• Eigenvalues and Eigenvectors
• Linear Algebra : Basic Definitions
• Eigenvalue Decompositon
• Principal Component Analysis and its Interpretations
• PCA : Interpretation 2
• PCA : Interpretation 3
• PCA : Interpretation 3 (Contd.)
• PCA : Practical Example
• Singular Value Decomposition
• Introduction to Autoncoders
• Link between PCA and Autoencoders
• Regularization in autoencoders (Motivation)
• Denoising Autoencoders
• Sparse Autoencoders
• Contractive Autoencoders
• Bias and Variance
• Train error vs Test error
• Train error vs Test error (Recap)
• True error and Model complexity
• L2 regularization
• Dataset augmentation
• Parameter sharing and tying
• Adding Noise to the inputs
• Adding Noise to the outputs
• Early stopping
• Ensemble Methods
• A quick recap of training deep neural networks
• Unsupervised pre-training
• Better activation functions
• Better initialization strategies
• Batch Normalization
• One-hot representations of words
• Distributed Representations of words
• SVD for learning word representations
• SVD for learning word representations (Contd.)
• Continuous bag of words model
• Skip-gram model
• Skip-gram model (Contd.)
• Contrastive estimation
• Hierarchical softmax
• GloVe representations
• Evaluating word representations
• Relation between SVD and Word2Vec
• The convolution operation
• Relation between input size, output size and filter size
• Convolutional Neural Networks
• Convolutional Neural Networks (Contd.)
• CNNs (success stories on ImageNet)
• CNNs (success stories on ImageNet) (Contd.)
• Image Classification continued (GoogLeNet and ResNet)
• Visualizing patches which maximally activate a neuron
• Visualizing filters of a CNN
• Occlusion experiments
• Finding influence of input pixels using backpropagation
• Guided Backpropagation
• Optimization over images
• Create images from embeddings
• Fooling Deep Convolutional Neural Networks
• Sequence Learning Problems
• Recurrent Neural Networks
• Backpropagation through time
• The problem of Exploding and Vanishing Gradients
• Some Gory Details
• Selective Read, Selective Write, Selective Forget - The Whiteboard Analogy
• Long Short Term Memory(LSTM) and Gated Recurrent Units(GRUs)
• How LSTMs avoid the problem of vanishing gradients
• How LSTMs avoid the problem of vanishing gradients (Contd.)
• Introduction to Encoder Decoder Models
• Applications of Encoder Decoder models
• Attention Mechanism
• Attention Mechanism (Contd.)
• Attention over images
• Hierarchical Attention
• Live Session 10-04-2021
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts

Deep Learning | Week 1

Course Name: Deep Learning

Course Link: Click Here

These are NPTEL Deep Learning Week 1 Assignment 1 Answers

Q1) From a pack of 52 cards, two cards are drawn together at random. What is the probability of both the cards being kings? a. 1/15 b. 25/57 c. 35/256 d. 1/221

Answer: d. 1/221

Q2) For a two class problem Bayes minimum error classifier follows which of following rule? (The two different classes are w₁ and w 2 , and input feature vector is x) a. Choose w₁ if P(w₁/x) > P(w 2 /x) b. Choose w₁ if P(w₁)>P(w 2 ) c. Choose w 2 if P(w₁)<P(w 2 ) d. Choose w 2 if P(w₁/x) > P(w 2 /x)

Answer: a. Choose w₁ if P(w₁/x) > P(w 2 /x)

Q3) The texture of the region provides measure of which of the following properties? a. Smoothness alone b. Coarseness alone c. Regularity alone d. Smoothness, coarseness and regularity

Answer: d. Smoothness, coarseness and regularity

Q4) Why convolution neural network is taking off quickly in recent times? (Check the options that are true.) a. Access to large amount of digitized data b. Integration of feature extraction within the training process. c. Availability of more computational power d. All of the above.

Answer: d. All of the above.

Q5) The bayes formula states : a. posterior = likelihood*prior/evidence b. posterior = likelihood*evidence/prior c. posterior = likelihood * prior d. posterior = likelihood * evidence

Answer: a. posterior = likelihood*prior/evidence

Q6) Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficient and use only first m (m<K) number of coefficient to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape? a. We will get a smoothed boundary version of the shape. b. We will get only the fine details of the boundary of the shape. c. Full shape will be reconstructed without any loss of information. d. Low frequency component of the boundary will be removed from contour of the shape.

Answer: a. We will get a smoothed boundary version of the shape.

Q7) The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as a. Signature descriptor b. Polygonal descriptor c. Fourier descriptor. d. Convex Hull

Answer: a. Signature descriptor

Q8) If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true? a. The value of element difference moment will be high. b. The value of inverse element difference moment will be high. c. The value of entropy will be very low. d. None of the above.

Answer: b. The value of inverse element difference moment will be high.

Q9) Which of the following is a Co-occurrence matrix based descriptor a. Entropy b. Uniformity c. Signature d. Inverse Element difference moment. e. All of the above.

Answer: e. All of the above.

Q10) Consider two class Bayes’ Minimum Risk Classifier. Probability of classes W1 and W2 are, P (w₁) =0.3 and P (w₂) =0.7 respectively. P(x) = 0.55, P (x| w₁) = 0.75, P (x| w2) =0.45 and the loss matrix values are

Find the Risk R (α₂|x). a. 0.42 b. 0.61 c. 0.48 d. 0.39

Answer: a. 0.42

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[week 1-12] nptel deep learning – iit ropar assignment answers 2024.

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NPTEL Deep Learning Assignment 1 Answers 2023

• by QuizXp Team
• July 23, 2023 July 23, 2023

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:-

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 feed forward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms.

NPTEL Deep Learning Assignment 1 Answers 2023:

1. The table below shows the temperature and humidity data for two cities. Is the data linearly separable?

Cannot be determined from the given information

Answer:- Will update answers soon and update on our telegram channel so Join Click Here

2. What is the perceptron algorithm used for?

Clustering data points

Finding the shortest path in a graph

Classifying data

Solving optimization problems

3. What is the most common activation function used in perceptrons?

Next Week Answers: Assignment 02

4. Which of the following Boolean functions cannot be implemented by a perceptron?

5. We are given 4 points in R2 say, x 1=(0,1), x 2=(−1,−1), x 3=(2,3), x 4=(4,−5).Labels of x 1, x 2, x 3, x 4 are given to be −1,1,−1,1 We initiate the perceptron algorithm with an initial weight w 0=(0,0) on this data. What will be the value of w 0 after the algorithm converges? (Take points in sequential order from x 1 to x )( update happens when the value of weight changes) (0,0)

7. Suppose we have a boolean function that takes 5 inputs x 1, x 2, x 3, x 4, x 5? We have an MP neuron with parameter θ =1. For how many inputs will this MP neuron give output y =1

8. Which of the following best represents the meaning of term “Artificial Intelligence”?

The ability of a machine to perform tasks that normally require human intelligence

The ability of a machine to perform simple, repetitive tasks

The ability of a machine to follow a set of pre-defined rules

The ability of a machine to communicate with other machines

9. Which of the following statements is true about error surfaces in deep learning?

They are always convex functions.

They can have multiple local minima.

They are never continuous.

They are always linear functions.

10. What is the output of the following MP neuron for the AND Boolean function?

y ={1,0,if  x 1+ x 2+ x 3≥1otherwise y =1 for ( x 1, x 2, x 3)=(0,1,1) y =0 for ( x 1, x 2, x 3)=(0,0,1) y =1 for ( x 1, x 2, x 3)=(1,1,1) y =0 for ( x 1, x 2, x 3)=(1,0,0)

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Dear Candidate,

Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2021 semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in Jan 2022 and we are giving you another chance to write the exam in April 2022 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc.

IMPORTANT instructions for learners - Please read this carefully  

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Please fill the exam form using the same Enrolled email id & make fee payment via the form, as before.

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programming exams.

6. If you do enroll to Jan 2022 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters

Our suggestion:

- Please check once if you have >= 40/100  in average assignment score and also participate in the non-proctored programming exams that will be conducted during this semester in the course to become eligible for the e-certificate, wherever applicable.

- If not, please submit Assignments again in the Jan 2022 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate.

- You can also submit Assignments again and participate in the non-proctored programming exams if you want to better your previous scores.

RECOMMENDATION: Please enroll to the Jan 2022 course and brush up your lessons for the exam.

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If you register for the exam and pay before March 14, 2022, 10:00 AM, Exam fees will be Rs. 1000/- per exam . 

If you register for exam before March 14, 2022, 10:00 AM and have not paid or if you register between March 14, 2022, 10:00 AM & March 18, 2022, 10:00 AM, Exam fees will be Rs. 1500/- per exam 

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Deep Learning - IIT Ropar: Open now for exam registration July 2021!!

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the  Jan 2021  semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in July 2021 and we are giving you another chance to write the exam in Sep/Oct 2021 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc. IMPORTANT instructions for learners - Please read this carefully 1. The exam date for this course:  October 23, 2021 2. Certification exam registration URL is:  https://examform.nptel.ac.in/     Please fill the exam form using the  same Enrolled email id  & make fee payment via the form, as before. 3. Choose from the Cities where exam will be conducted:   Exam Cities 4. You DO NOT have to re-enroll in the courses.  5. You DO NOT have to resubmit Assignments OR participate in the non-proctored  programming exams. 6. If you do enroll to July 2021 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters Our suggestion: - Please check once if you have >= 40/100  in average assignment score and also participate in the non-proctored programming exams that will be conducted during this semester in the course to become eligible for the e-certificate, wherever applicable. - If not, please submit Assignments again in the July 2021 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate. - You can also submit Assignments again and participate in the non-proctored programming exams if you want to better your previous scores. RECOMMENDATION:  Please enroll to the July 2021 course and brush up your lessons for the exam. 7.  Exam fees:  If you register for the exam and pay before  Sep 13, 2021, 10:00 AM , Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Sep 13, 2021, 10:00 AM  and have not paid or if you register between  Sep 13, 2021, 10:00 AM & Sep 17, 2021, 5:00 PM , Exam fees will be  Rs. 1500/-  per exam  8. 50% fee waiver for the following categories:  Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate. Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.  9. Last date for exam registration: Sep 17, 2021, 5:00 PM (Friday).   10. Mode of payment: Online payment - debit card/credit card/net banking.  11.  HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date  . We will confirm the same through an announcement once it is published.  12. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.  13.  Data changes:   Last date for data changes: Sep 17, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  14.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Sep 17, 2021, 5:00 PM   15. Click here to view Timeline and Guideline :  Guideline   Domain Certification Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.     Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain:  https://nptel.ac.in/noc/Domain/discipline.html Thanks & Regards,  NPTEL TEAM

April 2021 NPTEL Exams have been postponed!

Dear learner Taking the current covid situation into consideration, the NPTEL exams scheduled to be conducted on 24/25 April stand postponed until further notice. We will keep you informed of the potential dates for the exams as the situation improves and we finalize the same. Thanks and Regards, NPTEL TEAM.

Exam Format - April 25,2021

Dear Candidate, ****This is applicable only for the exam registered candidates**** Type of exam will be available in the list: Click Here You will have to appear at the allotted exam center and produce your Hall ticket and Government Photo Identification Card (Example: Driving License, Passport, PAN card, Voter ID, Aadhaar-ID with your Name, date of birth, photograph and signature) for verification and take the exam in person.  You can find the final allotted exam center details in the hall ticket. The hall ticket is yet to be released . We will notify the same through email and SMS. Type of exam: Computer based exam (Please check in the above list corresponding to your course name) The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay-type answers, etc. Type of exam: Paper and pen Exam  (Please check in the above list corresponding to your course name) The questions will be on the computer. You will have to write your answers on sheets of paper and submit the answer sheets. Papers will be sent to the faculty for evaluation. On-Screen Calculator Demo Link: Kindly use the below link to get an idea of how the On-screen calculator will work during the exam. https://tcsion.com/ OnlineAssessment/ ScientificCalculator/ Calculator.html NOTE: Physical calculators are not allowed inside the exam hall. -NPTEL Team

Deep Learning - IIT Ropar : Week 12 Feedback Form

Deep learning - iit ropar : week 12 is live now .

Dear students The lecture videos for Week-12 have been uploaded for the course  Deep Learning - IIT Ropar  . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=161&lesson=162 Assignment for Week-12 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=161&assessment=190 The assignment has to be submitted on or before  Wednesday, [14-04-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 11 Feedback Form

Deep learning - iit ropar : week 11 is live now .

Dear students The lecture videos for Week-11 have been uploaded for the course  Deep Learning - IIT Ropar  . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=149&lesson=150 Assignment for Week-11 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=149&assessment=189 The assignment has to be submitted on or before  Wednesday, [07-04-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 10 Feedback Form

Deep learning - iit ropar : week 10 is live now .

Dear students The lecture videos for Week-10 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=129&lesson=130 Assignment for Week-10 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=129&assessment=188 The assignment has to be submitted on or before Wednesday, [31-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 9 Feedback Form

Deep learning - iit ropar : week 9 is live now .

Dear students The lecture videos for Week-9 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=114&lesson=115 Assignment for Week-9 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/assessment?name=187 The assignment has to be submitted on or before Wednesday, [24-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 8 Feedback Form

Deep learning - iit ropar : week 8 is live now .

Dear students The lecture videos for Week-8 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=106&lesson=107 Assignment for Week-8 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/assessment?name=186 The assignment has to be submitted on or before Wednesday, [17-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 7 Feedback Form

Deep learning - iit ropar : week 7 is live now .

Dear students The lecture videos for Week-7 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=91&lesson=92 Assignment for Week-7 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=91&assessment=185 The assignment has to be submitted on or before Wednesday, [10-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 6 Feedback Form

Deep learning - iit ropar : week 6 is live now .

Dear students The lecture videos for Week-6 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=82&lesson=83 Assignment for Week-6 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/assessment?name=184 The assignment has to be submitted on or before Wednesday, [03-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 5 Feedback Form

Deep learning - iit ropar : assignment 1 reevaluations.

Dear Learners,   Assignment 1 submissions of all students have been reevaluated  by making the weightage as 0 for Q6 and changing the answer for Q9. Students are requested to find their revised scores of Assignment 1 in the Progress page. Thanks & Regards, -NPTEL Team.

Deep Learning - IIT Ropar : Week 5 is live now !!

Dear students The lecture videos for Week-5 have been uploaded for the course  Deep Learning - IIT Ropar   . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=70&lesson=71 Assignment for Week-5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/assessment?name=183 The assignment has to be submitted on or before Wednesday, [24-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 4 Feedback Form

Deep learning - iit ropar : week 4 is live now .

Dear students The lecture videos for Week-4 have been uploaded for the course  Deep Learning - IIT Ropar. The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=58&lesson=59 Assignment for Week-4 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=58&assessment=182 The assignment has to be submitted on or before Wednesday, [17-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Feedback on Text Transcripts (English) of NPTEL videos

Dear Learners, We have uploaded the English transcripts for this course already. We would like to hear from you, a quick feedback for the same. Please take a minute to fill out this form. Click here  to fill the form -NPTEL Team

Deep Learning - IIT Ropar : Week 3 Feedback Form

Deep learning - iit ropar : week 3 is live now .

Dear students The lecture videos for Week-3 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=45&lesson=46 Assignment for Week-3 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=45&assessment=181 The assignment has to be submitted on or before Wednesday, [10-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Assignment 2 due date has been extended!!

Dear Learners, Assignment 2 has been released already and the due date for the assignment has been extended Due date of assignment 2 is Sunday, 07-02-2021, 23:59 IST Please note that there will not be any extension for the upcoming assignments. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately.   Thanks & Regards, - NPTEL Team

Deep Learning - IIT Ropar : Week 2 Feedback Form

Deep learning - iit ropar : week 2 is live now .

Dear students The lecture videos for Week-2 have been uploaded for the course  Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=35&lesson=36 Assignment for Week-2 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/assessment?name=180 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Week 1 Feedback Form

Deep learning - iit ropar : week 1 is live now .

Dear students The lecture videos for Week-1 have been uploaded for the course  Deep Learning - IIT Ropar  . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=17&lesson=18 Assignment for Week-1 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=17&assessment=177 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks & Regards, -NPTEL Team

Deep Learning - IIT Ropar : Revised Assignment 0 is live now !!

Dear Learners,  We welcome you all to this  Deep Learning - IIT Ropar  course. The assignment 0 has been released.  This assignment is based on prerequisite of the course.  You can find the assignment in the link below :  https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=175&assessment=176 Due date of assignment 0 is  25-01-2021, 23:59 IST Please note that this assignment is for practice and it will not be graded. - Regards   NPTEL Team

NPTEL: Exam Registration is open now for Jan 2021 courses!

Dear Learner,  Here is the much-awaited announcement on registering for the Jan 2021 NPTEL course certification exam.  1. The registration for the certification exam is open only to those learners who have enrolled in the course.  2. If you want to register for the exam for this course, login here using the same email id which you had used to enroll to the course in Swayam portal. Please note that Assignments submitted through the exam registered email id ALONE will be taken into consideration towards final consolidated score & certification.  3 .  Date of exam: April 25, 2021 Certification exam registration URL is:  https://examform.nptel.ac. in/   Choose from the Cities where exam will be conducted:  Exam Cities   4. Exam fees:  If you register for the exam and pay before  Mar 8, 2021, 10:00 AM,  Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Mar 8, 2021 , 10:00 AM  and have not paid or if you register between  Mar 8, 2021, 10:00 AM & Mar 12, 2021, 5:00 PM,  Exam fees will be  Rs. 1500/-  per exam  5. 50% fee waiver for the following categories:  Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate. Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.  6. Last date for exam registration: Mar 12, 2021 5:00 PM (Friday).  7. Mode of payment: Online payment - debit card/credit card/net banking.  8. HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date .  We will confirm the same through an announcement once it is published.  9. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.  10.  Data changes:  Last date for data changes: Mar 12, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  11.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Mar 12, 2021, 5:00 PM  12. Click here to view Timeline and Guideline :  Guideline    Thanks & Regards, NPTEL TEAM

Deep Learning - IIT Ropar : Week 1 videos are live now!!

Dear Learners, The lecture videos for Week-1 have been uploaded for the course Deep Learning - IIT Ropar . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs35/unit?unit=17&lesson=18 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already).   As we have done so far, please use the discussion forums if you have any questions on this module. - NPTEL Team

NPTEL : Keep in touch with us via Social Media

Dear Learner You already must know NPTEL is providing course certificates to those who complete the course successfully, with the learning happening right at your home or where you are. But NPTEL also keeps bringing out new initiatives and courses - which we would like to keep you posted on. Click the below links to like and follow us on Social Media for instant Updates: Facebook: https://www.facebook.com/NPTELNoc Twitter: https://twitter.com/nptelindia Linkedin: https://www.linkedin.com/in/nptel-india-085866ba/ Instagram: https://www.instagram.com/swayam_nptel/  Like and Follow us on Social Media. Let's create a better future by learning and growing together.  -NPTEL Team.

Welcome to NPTEL Online Course - Jan 2021!!

• Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this. Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
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[Week 3] NPTEL Deep Learning – IIT Ropar Assignment Answer 2023

NPTEL Deep Learning – IIT Ropar Assignment Answer

Table of Contents:-

NPTEL Deep Learning – IIT Ropar Week 3 Assignment Answer 2023

1. Which of the following statements about backpropagation is true?

• It is used to optimize the weights in a neural network.
• It is used to compute the output of a neural network.
• It is used to initialize the weights in a neural network.
• It is used to regularize the weights in a neural network.

2. Let y be the true class label and p be the predicted probability of the true class label in a binary classification problem. Which of the following is the correct formula for binary cross entropy?

3. Let yi�� be the true class label of the i�-th instance and pi�� be the predicted probability of the true class label in a multi-class classification problem. Write down the formula for multi-class cross entropy loss.

4. Can cross-entropy loss be negative between two probability distributions?

5. Let p� and q� be two probability distributions. Under what conditions will the cross entropy between p� and q� be minimized?

• All the values in p� are lower than corresponding values in q�
• p� = 0 [0 is a vector]

6. Which of the following is false about cross-entropy loss between two probability distributions? It is always in range (0,1) It can be negative. It is always positive. It can be 1.

7. The probability of all the events x1,x2,x2….xn in a system is equal(n>1 ). What can you say about the entropy H(X) of that system?(base of log is 2)

• We can’t say anything conclusive with the provided information.

8. Suppose we have a problem where data x and label y are related by y=x4+1 . Which of the following is not a good choice for the activation function in the hidden layer if the activation function at the output layer is linear?

9. We are given that the probability of Event A happening is 0.95 and the probability of Event B happening is 0.05. Which of the following statements is True?

• Event A has a high information content
• Event B has a low information content
• Event A has a low information content
• Event B has a high information content

10. Which of the following activation functions can only give positive outputs greater than 0?

NPTEL Deep Learning – IIT Ropar Week 2 Assignment Answer 2023

1. What is the range o f the sigmoid function σ(x)=1/1+e −x ?

2. What happens to the output of the sigmoid function as |x| very small?

• The output approaches 0.5
• The output approaches 1.
• The output oscillates between 0 and 1.
• The output becomes undefined.

3. Which of the following theorem states that a neural network with a single hidde n layer containing a finite number of neurons can approximate any continuous function?

• Bayes’ theorem
• Central limit theorem
• Fourier’s theorem
• Universal approximation theorem

4. We have a function that we want to a pproximate using 150 rectangles (towers). How many neurons are required to construct the required network?

5. A neural network has two hidden layers with 5 neurons in each layer, and an output la y er with 3 neurons, and an input layer with 2 neurons. How many weights are there in total? (Dont assume any bias terms in the network)

6. What is the derivative of the ReLU activation function with respect to its i nput at 0?

• Not differentiable

7. Consider a function f(x)=x 3 −3x 2 +2. What is the updated value of xafter 3rd iteration of the gradient descent update, if the learning rate is 0.10.1 and the initial value of x is 4?

8. Which of the following statements is true about the representation power of a multilayer netw o rk of sigmoid neurons?

• A multilayer network of sigmoid neurons can represent any Boolean function.
• A multilayer network of sigmoid neurons can represent any c ontinuous function.
• A multilayer network of sigmoid neurons can represent any function.
• A multilayer network of sigmoid neurons can represent any linear function.

9. How many boolean function s can be designed for 3 inputs?

10. How many neurons do you need in the hidden layer of a perceptron to learn any boo l ean function with 6 inputs? (Only one hidden layer is allowed)

NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answer 2023

• Cannot be determined from the given information

2. What is the perceptron algorithm used for?

• Clustering data points
• Finding the shortest path in a graph
• Classifying data
• Solving optimization problems

3. What is the most common activation function used in perceptrons?

4. Which of the following Boolean functions cannot be implemented by a perceptron?

5. We are given 4 points in R2 say, x1=(0,1),x2=(−1,−1),x3=(2,3),x4=(4,−5).Labels of x1,x2,x3,x4 are given to be −1,1,−1 , 1 We initiate the perceptron algorithm with an initial weight w0=(0,0) on this data. What will be the value of w0 after the algorithm converges? (Take points in sequential order from x1 to x)( update happens when the value of weight changes)

7. Suppose we have a boolean function that takes 5 inputs x1,x2,x3,x4,x5? We have an MP neuron with parameter θ=1. For how many inputs will this MP neuron give output y=1?

8. Which of the following best represents the meaning of term “Artificial Intelligence”?

• The ability of a machine to perform tasks that normally require human intelligence
• The ability of a machine to perform simple, repetitive tasks
• The ability of a machine to follow a set of pre-defined rules
• The ability of a machine to communicate with other machines

9. Wh i ch of the following statements is true about error surfaces in deep learning?

• They are always convex functions.
• They can have multiple local minima.
• They are never continuous.
• They are always linear functions.

10. What is the output of the following MP neuron for the AND Boolean function?

y={1,0,if x1+x2+x3≥1 0, therwise

• y=1 for (x1,x2,x3)=(0,1,1)
• y=0 for (x1,x2,x3)=(0,0,1)
• y=1 for (x1,x2,x3)=(1,1,1)
• y=0 for (x1,x2,x3)=(1,0,0)

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