Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Deep Learning

CVBL Home CVBL Courses

Offerings

• Deep Learning, Jan-June 2023
• Deep Learning, July-Dec 2022
• Deep Learning, July-Dec 2021

Course Information

Objective of the course: To get the students and researchers exposed to the state-of-the-art deep learning techniques, approaches and how to optimize their results to increase its efficiency and get some hands-on on the same to digest the important concepts.

Outcome of the course: As deep learning has demonstrated its tremendous ability to solve the learning and recognition problems related to the real world problems, the software industries have accepted it as an effective tool. As a result there is a paradigm shift of learning and recognition process. The students and researchers should acquire knowledge about this important area and must learn how to approach to a problem, whether to deal with deep learning solution or not. After undergoing this course they should be able to categorize which algorithm to use for solving which kind of problem. Students will be able to find out the ways to regularize the solution better and optimize it as per the problem requirement. Students will be exposed to the background mathematics involved in deep learning solutions. They will be able to deal with real time problems and problems being worked upon in industries. Taking this course will substantially improve their acceptability to the machine learning community – both as an intelligent software developer as well as a matured researcher.

Course Instructors

Dr. Satish Kumar Singh

Dr. Shiv Ram Dubey

Prerequisites

• Computer Programming
• Data Structures and Algorithms
• Machine Learning
• Image and Video Processing
• Ability to deal with abstract mathematical concepts

Books

• Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press

Related Classes / Online Resources

• CS231n Convolutional Neural Networks for Visual Recognition, Stanford
• Deep Learning, Coursera
• Deep Learning, New York University
• Deep Learning, CMU

Disclaimer

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