Courses » Deep Learning For Visual Computing

Deep Learning For Visual Computing

Deep learning is a genre of machine learning algorithms that attempt to solve tasks by learning abstraction in data following a stratified description paradigm using non-­linear transformation architectures. When put in simple terms, say you want to make the machine recognize Mr. X standing in front of Mt. E on an image;; this task is a stratified or hierarchical recognition task. At the base of the recognition pyramid would be features which can discriminate flats, lines, curves, sharp angles, color;; higher up will be kernels which use this information to discriminate body parts, trees, natural scenery, clouds, etc.;; higher up it will use this knowledge to recognize humans, animals, mountains, etc.;; and higher up it will learn to recognize Mr. X and Mt. E and finally the apex lexical synthesizer module would say that Mr. X is standing in front of Mt. E. Deep learning is all about how you make machines synthesize this hierarchical logic and also learn these representative features and kernels all by itself. It has been used to solve problems like handwritten character recognition, object and product recognition and localization, image captioning, generating synthetic images to self driving cars. This course would provide you insights to theory and coding practice of deep learning for visual computing through curated exercises with Python and PyTorch on current developments.

  • Electrical
  • Electronics
  • Computer Sciences
  • Digital Image Processing
  • Machine Learning



  • Industry related to Deep Learning and Machine Vision such as Intel
  • Microsoft
  • Google
  • Nvidia
  • Philips
  • GE
  • Siemens
  • Samsung
  • IBM
  • Apple
  • TCS
  • Infosys
  • Wipro
  • Robert Bosch
  • Baidu
  • Wymo
  • Tesla, etc. 

2607 students have enrolled already!!


Debdoot Sheeet  is an Assistant Professor of Electrical Engineering at the Indian Institute of Technology Kharagpur and founder of SkinCurate Research. He received the MS and PhD degrees in computational medical imaging and machine learning from the Indian Institute of Technology Kharagpur in 2010 and 2014 respectively. He was a DAAD visiting PhD scholar to TU Munich during 2011-­12. His research interests include deep learning and domain adaptation, computational medical imaging, image and multidimensional signal processing, surgical analytics and informatics, visualization and augmented reality technology design. He has widely published in journals including Medical Image Analysis (MedIA), and conferences like the IEEE International Symposium on Biomedical Imaging (ISBI). He is a member of IEEE, SPIE, ACM, IUPRAI and BMESI and serves as an Editor of IEEE Pulse since 2014.

Week 1: Introduction to Visual Computing and Neural Networks

Week 2: Multilayer Perceptron to Deep Neural Networks with Autoencoders
Week 3: Autoencoders for Representation Learning and MLP Initialization

Week 4: Stacked, Sparse, Denoising Autoencoders and Ladder Training

Week 5: Cost functions, Learning Rate Dynamics and Optimization

Week 6: Introduction to Convolutional Neural Networks (CNN) and LeNet

Week 7: Convolutional Autoencoders and Deep CNN (AlexNet, VGGNet)

Week 8: Very Deep CNN for Classification (GoogLeNet, ResNet, DenseNet)

Week 9: Computational Complexity and Transfer Learning of a Network

Week 10:Object Localization (RCNN) and Semantic Segmentation

Week 11:Generative Models with Adversarial Learning

Week 12: Recurrent Neural Networks (RNN) for Video Classification


1. Goodfellow, Y, Bengio, A. Courville, “Deep Learning”, MIT Press, 2016. S. Haykin, “Neural Networks and Learning Machines”,3e,Pearson, 2008.

  • The exam is optional for a fee. · 
  • Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) :
    Afternoon session: 2pm to 5pm.
  • Exam for this course will be available in one session on both 28 and 29 April.  
  • Registration url: Announcements will be made when the registration form is open for registrations.  
  • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. 

  • Final score will be calculated as : 25% assignment score + 75% final exam score  
  • 25% assignment score is calculated as 25% of average of 12 weeks course: Best 8 out of 12 assignments  
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of NPTEL and IIT Kharagpur. It will be e-verifiable at nptel.ac.in/noc.