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Deep Learning - IIT Ropar

By Prof. Sudarshan Iyengar, Prof. Sanatan Sukhija   |   IIT Ropar, Mahindra University, Hyderabad
Learners enrolled: 5544
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

INTENDED AUDIENCE: Any Interested Learners
PREREQUISITES:          Working knowledge of Linear Algebra, Probability Theory. It would be beneficial if the participants have done a course on Machine Learning.


NOTE: This course videos are created and offered earlier by Prof. Mitesh Khapra, it will be offfered by Prof. Sudarshan Iyengar for the subsequent runs.

Summary
Course Status : Completed
Course Type : Elective
Duration : 12 weeks
Start Date : 24 Jan 2022
End Date : 15 Apr 2022
Exam Date : 24 Apr 2022 IST
Enrollment Ends : 07 Feb 2022
Category :
  • Computer Science and Engineering
  • Artificial Intelligence
  • Data Science
  • Robotics
Credit Points : 3
Level : Undergraduate/Postgraduate

Page Visits



Course layout

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

Instructor bio

Prof. Sudarshan Iyengar

IIT Ropar
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

Mahindra University, Hyderabad
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.)

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 24 April 2022 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

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.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team


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