Course Name: Deep Learning - IITKGP

Course abstract

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.


Course Instructor

Media Object

Prof. Prabir Kumar Biswas

Dr. Prabir Kr. Biswas completed his B.Tech(Hons), M.Tech and Ph.D from the Department of Electronics and Electrical Communication Engineering, IIT Kharagpur, India in the year 1985, 1989 and 1991 respectively. From 1985 to 1987 he was with Bharat Electronics Ltd. Ghaziabad as a deputy engineer. Since 1991 he has been working as a faculty member in the department of Electronics and Electrical Communication Engineering, IIT Kharagpur, where he is currently holding the position of Professor and Head of the Department. Prof. Biswas visited University of Kaiserslautern, Germany under the Alexander von Humboldt Research Fellowship during March 2002 to February 2003. Prof. Biswas has more than a hundred research publications in international and national journals and conferences and has filed seven international patents. His area of interest are image processing, pattern recognition, computer vision, video compression, parallel and distributed processing and computer networks. He is a senior member of IEEE and was the chairman of the IEEE Kharagpur Section, 2008.
More info

Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Jan-Apr 2021

  View Course

 Syllabus

 Enrollment : 18-Nov-2020 to 25-Jan-2021

 Exam registration : 15-Jan-2021 to 12-Mar-2021

 Exam Date : 24-Apr-2021

Enrolled

10001

Registered

538

Certificate Eligible

279

Certified Category Count

Gold

11

Silver

79

Elite

95

Successfully completed

94

Participation

53

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Note:
We have taken best assignment score from both Jan 2020 and Jan2021 course
Deep Learning - IITKGP - Toppers list
Top 1 % of Certified Candidates

USHNISH SARKAR 94%

HOMI BHABHA NATIONAL INSTITUTE

GANNAVARAPU VEERA VENKATA NAGA RAJESH 92%

Vestas Wind Technology R&D Chennai Pvt. Ltd.

DEEPAK PUTREVU 91%

Indian Space Research Organization

PRAVEER SAXENA 91%

NATIONAL INSTITUTE OF TECHNOLOGY PATNA

SAYYAD MOHD ZAKARIYA 91%

University Polytechnic, Aligarh Muslim University


Top 2 % of Certified Candidates

RAMYA R 90%

KAMARAJ COLLEGE OF ENGINEERING AND TECHNOLOGY

KETAN ANAND 90%

M S RAMAIAH INSTITUTE OF TECHNOLOGY

AARTHI V S 90%

SRI SIVASUBRAMANIYA NADAR COLLEGE OF ENGINEERING

KAMALAKAR BAPANAPALLI 90%

Sravanam Keerthanam Smaranam

PRIYANKA 90%

NATIONAL INSTITUTE OF TECHNOLOGY PATNA

JEWALIDDIN SHAIK 90%

NIT Andhra Pradesh


Top 5 % of Certified Candidates

PRABU S 88%

SASTRA DEEMED TO BE UNIVERSITY

DONGA ROHIT SAI 88%

INDIAN INSTITUTE OF TECHNOLOGY,TIRUPATI

SAYANTAN SARKAR 88%

NA

RISHABH SHARMA 88%

NATIONAL INSTITUTE OF TECHNOLOGY PATNA

AYUSH NATH JHA 88%

NATIONAL INSTITUTE OF TECHNOLOGY PATNA

Enrollment Statistics

Total Enrollment: 10001

Registration Statistics

Total Registration : 539

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.