Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Deep Learning

July-Dec 2022 Semester

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

Teaching Assistants



Nand Kumar Yadav

Albert Mundu

Pravin Kumar


Class schedule
Lecture: Thursday (CC3-5107, 11.00 am - 01.00 pm), Tute: Friday (CC3-5207, 11.00 am - 01.00 pm), Practice: Thursday (CC3-5422, 05.00 pm - 07.00 pm)

Schedule - Lectures

Date Topic Optional Reading
L00 Course Introduction
L01-02 Linear Machines and Learning
L03-05 Classifiers
L06-08 Neural Networks
    L09 Deep Learning: Introduction, Motivation and Status
      L10 Convolutional Neural Networks
        L11 CNN Performance
          L12 Activation Function
            L13-15 Training Aspects of Neural Networks
              L16-17 CNN Architectures for Image Classification
                L18-19 CNN Architectures for Object Detection
                  L20-21 CNN Architectures for Image Segmentation
                    L22-23 Generative Adversarial Network (GAN)
                    Conditional GAN
                      L24-25 Recurrent Neural Network (RNN)
                        L26-27 Attention in Neural Network
                        Transformer Network

                          Grading

                          • C1 (30%)
                          • C2 (30%)
                          • C3 (40%)

                          Prerequisites

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

                          Books

                          Related Classes / Online Resources

                          Disclaimer

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