Course Name: 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

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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
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Prof. Sanatan Sukhija

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.)

Teaching Assistant(s)

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 Course Duration : Jan-Apr 2022
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 Enrollment : 14-Nov-2021 to 31-Jan-2022
 Exam registration : 13-Dec-2021 to 18-Mar-2022
 Exam Date : 24-Apr-2022

Enrolled

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Registered

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Certificate Eligible

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Certified Category Count

Gold

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Silver

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Elite

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Successfully completed

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Participation

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Success

Elite

Gold




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Final Score Calculation Logic

Enrollment Statistics

Total Enrollment: 5541

Assignment Statistics



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.