Course Name: Deep Learning - Part 2

Course abstract

In this course, we will cover topics which lie at the intersection of Deep Learning and Generative Modeling. We will start with basics of joint distributions and build up to Directed and Undirected Graphical Models. We will then make a connection between Graphical Models and Deep Learning by having an in-depth discussion on Restricted Boltzmann Machines, Markov Chains and Gibbs Sampling for training RBMs. Finally, we will cover more recent Deep Generative models such as Variational Autoencoders, Generative Adversarial Networks and Autoregressive Models.


Course Instructor

Media Object

Prof. Mitesh M. Khapra

Mitesh M. Khapra is an Assistant Professor in the Department of Computer Science and Engineering at IIT Madras. While at IIT Madras he plans to pursue his interests in the areas of Deep Learning, Multimodal Multilingual Processing, Dialog systems and Question Answering. Prior to that he worked as a Researcher at IBM Research India. During the four and half years that he spent at IBM he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. This work led to publications in top conferences in the areas of Computational Linguistics and Machine Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively. His PhD thesis dealt with the important problem of reusing resources for multilingual computation.
More info

Teaching Assistant(s)

Nitesh Methani

MS, Computer Science

Pritha Ganguly

MS, Computer Science

 Course Duration : Feb-Apr 2019
  View Course
 Syllabus
 Enrollment : 15-Nov-2018 to 25-Feb-2019
 Exam registration : 25-Feb-2019 to 19-Apr-2019
 Exam Date : 28-Apr-2019, 28-Apr-2019

Enrolled

5925

Registered

273

Certificate Eligible

159

Certified Category Count

Gold

1

Silver

46

Elite

66

Successfully completed

46

Participation

14

Success

Elite

Silver

Gold




Legend

>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed
<40 - No Certificate

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Deep Learning - Part 2 - Toppers list
Top 1 % of Certified Candidates

PRASHANT BARTAKKE 93%

COLLEGE OF ENGINEERING PUNE

AKHIL POOJARY 88%

INDIAN INSTITUTE OF TECHNOLOGY,MADRAS

MD DANISH IMAM 88%

INDIAN INSTITUTE OF TECHNOLOGY,KHARAGPUR

BISWAJIT SAHOO 88%

INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR.


Top 2 % of Certified Candidates

Top 5 % of Certified Candidates

DIPJYOTI BISHARAD 86%

NOKIA

DHARMA REDDY R 86%

VASAVI COLLEGE OF ENGINEERING

ADARSH K 86%

INDIAN INSTITUTE OF SPACE SCIENCE AND TECHNOLOGY

SARTHAK PESHWE 85%

INDIAN INSTITUTE OF TECHNOLOGY MADRAS, IIT-MADRAS, CHENNAI

MEENAL VIJAY NARKHEDE 85%

COLLEGE OF ENGINEERING PUNE

SHIRSHENDRANATH PAUL 85%

G H RAISONI INSTITUTE OF ENGINEERING AND TECHNOLOGY,NAGPUR

ANOOP C S 85%

GOVERNMENT ENGINEERING COLLEGE, PALAKKAD

Enrollment Statistics

Total Enrollment: 5925

Registration Statistics

Total Registration : 273

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.