Deep Learning IITKGP

By Prof. Prabir Kumar Biswas   |   IIT Kharagpur
Learners enrolled: 8747
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. INTENDED AUDIENCE: Electronics and Communication Engineering, Computer Science, Electrical Engineering PRE-REQUISITES: Knowledge of Linear Algebra, DSP, PDE will be helpful. INDUSTRY SUPPORT: Google, Adobe, TCS, DRDO etc.
Course Status : Completed
Course Type : Elective
Duration : 12 weeks
Start Date : 29 Jul 2019
End Date : 18 Oct 2019
Exam Date : 16 Nov 2019 IST
Category :
  • Computer Science and Engineering
Credit Points : 3
Level : Postgraduate

Page Visits

Course layout

Week 1:  Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
Week 2:  Linear Classifiers, Linear Machines with Hinge Loss
Week 3:  Optimization Techniques, Gradient Descent, Batch Optimization
Week 4:  Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
Week 5:  Unsupervised Learning with Deep Network, Autoencoders
Week 6:  Convolutional Neural Network, Building blocks of CNN, Transfer Learning
Week 7:  Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
Week 8:  Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
Week 9:  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: LSTM Networks
Week 12: Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

Books and references

1.Deep Learning- Ian Goodfelllow, Yoshua Benjio, Aaron Courville, The MIT Press 2.Pattern Classification- Richard O. Duda, Peter E. Hart, David G. Stork, John Wiley & Sons Inc.

Instructor bio

Prof. Prabir Kumar Biswas received his B.Tech., M.Tech., and Ph.D. degrees in Electronics and Electrical Communication Engineering from the Indian Institute of Technology Kharagpur in 1985, 1989, and 1991 respectively. He served Bharat Electronics Ltd. (BEL), Ghaziabad as a Deputy Engineer during the period 1985 to 1987. In 1991 he joined the faculty of the Department of Electronics and Electrical Communication Engineering at IIT Kharagpur where he is presently a Professor and also holding the position of Head of the Department. He also served as the Head of the Computer and Informatics Center at IIT Kharagpur from March 2008 to December 2014. Prof. Biswas visited the University of Kaiserslautern, Germany during March 2002 to February 2003 as Alexander von Humboldt Fellow. His research interests are in Image and Video Processing, Pattern Recognition, Machine Learning, Multimedia Systems, Cyber Physical Systems etc. He has published more than 100 research papers in various international and national journals and conference proceedings in these areas and also has a number of international patents in his credit. He has prepared four online Video Courses under NPTEL program. Prof. Biswas is a Senior Member of the IEEE. He also the president of Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI, India). He has served as a member of technical program committees and organizing committees of several national and international conferences, and served as Organizing Chair of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) in 2008.

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 centers.
  • The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
  • Date and Time of Exams: 16th November 2019 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.

  • 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

  • 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 Kharagpur. It will be e-verifiable at nptel.ac.in/noc.
  • Only the e-certificate will be made available. Hard copies are being discontinued from July 2019 semester and will not be dispatched.

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