NOC | Deep Learning

Course abstract

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

Course Instructor

Prof. Sudarshan Iyengar

Sudarshan Iyengar has a PhD from the Indian Institute of Science and is currently working as an assistant professor at IIT Ropar and has been teaching this course from the past 4 years.

More info

Prof. Padmavati

Dr. Padmavati received B.E degree in Computer Science & Engineering with distinction from PDACE, Gulbarga, V.T.U, Belgaum, Karnataka, M.E. degree in Computer Science & Engineering and Ph.D from Panjab Engineering College (Deemed to be University), Chandigarh. Currently, she is working as Assistant Professor at Panjab Engineering College (Deemed to be University), Chandigarh, India. She has been teaching this course since two years for M.Tech students. She has also offered courses like Data structure, Analysis and design of algorithms, Object oriented programming, Research methodology, and Wireless sensor networks. Her research interests are in the areas of Wireless sensor networks, IoT, machine learning and deep learning. Her current research projects include Classification of Parkinsons disease using machine learning algorithms, Major depressive disorder using EEG Signal, and Detection of neurological disorder - Epilepsy using EEG Signals.

Teaching Assistant(s)

No teaching assistant data available for this course yet

Course Duration : Jul-Oct 2021

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Syllabus

Enrollment : 20-May-2021 to 02-Aug-2021

Exam registration : 17-Jun-2021 to 17-Sep-2021

Exam Date : 23-Oct-2021

Enrolled

7345

Registered

804

Certificate Eligible

430

Certified Category Count

Gold

Silver

Elite

148

Successfully completed

170

Participation

252

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 2021 and July 2021 courses

Deep Learning - Toppers list

Top 1 % of Certified Candidates