Computer vision is an interesting area of Machine Learning and Deep Learning that still needs to evolve a lot to match human-level accuracy. So, I believe to inorder to increase the community strength for computer vision, we need more people equipped to work in this field and hence such a session on computer vision will be important. Hence this session will be a hands-on workshop on image generation in computer vision using deep learning algorithms and concepts and how it can be extended for video generation.


The objective of this workshop will be to teach the audience on the following concepts of Computer Vision:

  1. Introductory concepts and algorithms on Computer Vision required for image generation
  2. Neural Style Transfer
  3. Image Generation
  4. Introduction to video generation

The target will be to teach the audience with introductory to intermediate level of knowledge on the above topics. Based on the audience background and interest, the level of details can be modified.

Programming language will be Python. Preferred framework can be keras, tensorflow and open cv. Preferred medium of training will be Anaconda Spyder IDE or Jupyter notebook.

The idea is to teach the fundamental concepts in each area, show sample code-snippet for each applications and provide equivalent assignments to the audience, so that the audience can learn by hands-on coding and gain practical experience on this area.

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Outline/Structure of the Workshop

The rough structure of the workshop will look like:

  1. Introduction to Computer Vision using Deep Learning
    1. Brief discussion on Computer Vision with Classical Machine Learning approach
    2. Fundamental concepts of computer vision
  2. Neural Style Transfer
  3. Image Generation
    1. Using Generative Adversarial Networks
    2. Using Variational Auto Encoders
  4. Introduction to Video Generation using DCGAN and VAEs

I am planning to give small problem statements/assignments/challenges which the audiences have to complete on each of these topics along with discussion/lecture/practical demo, so that the audience can receive hands-on experience

Learning Outcome

  1. Learn and understand popular concepts and topics related to computer vision using deep learning
  2. Hands-on experience on computer vision using Python and popular frameworks
  3. Gain experience in handling image data
  4. Gain experience in working with interactive problem statements and challenges or mini projects
  5. Learn and work on some of the latest algorithms on Computer Vision with deep learning

Target Audience

Data Scientists, Engineers, Developers, AI Enthusiasts, particularly Computer Vision enthusiasts

Prerequisites for Attendees

  1. Basic knowledge on Machine Learning
  2. Basic knowledge in Python programming
  3. Previous knowledge of working with Jupyter notebook or Spyder IDE
  4. Basic knowledge of Neural Networks
  5. Basic knowledge on Deep Learning and Computer Vision
schedule Submitted 1 month ago

Public Feedback

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  • Joy Mustafi
    By Joy Mustafi  ~  2 weeks ago
    reply Reply

    Can you please include more on image / video generation instead of analysis and fundamentals of computer vision. Also if you can convert into 90 or 45 minutes session. Thanks, Joy

    • Aditya Bhattacharya
      By Aditya Bhattacharya  ~  2 weeks ago
      reply Reply


      I have updated the content to target this hands-on workshop particularly about Image generation using DCGAN and VAEs and how it can be extended towards video generation. Please feel free to let me know if I should try to include any other specific topic which will be helpful for the audience.



    • Aditya Bhattacharya
      By Aditya Bhattacharya  ~  2 weeks ago
      reply Reply

      Thanks Joy for the feedback. Sure I can do that. In that case, I will target only the specific area of image generation in computer vision using DCGAN and VAEs and how it can be extended towards Video Generation.

      And yes ofcourse, the session duration will also be shorter, and will be within 90 minutes.

      Let me update the content and the topic as well, and once done I will let you know. Please feel free to provide your feedback on the updated content, once it is done.

      Best Regards,