GAN - The tool of the future!

Moinak Bose (~moinak)


The paper of Ian Goodfellow on General Adversarial Network has opened a whole new dimension for machine learning. GAN starts its roots from Generative Modeling and now is progressing fast to attain the category of being a domain all to itself!

How can GAN be a determining factor in the field of technology? What are the challenges one has to face working in this field? What are the reasons behind it? Let’s find out together.

Work on creating something new from the hands of artificial intelligence has been an integral part since the very inception of Machine Learning. From CNN to DNN to GAN, there still stays loads of new methods for us to think about. An insight into the hot topic in the market of Machine Learning would only prepare us for the future of this field.

The talk aims to be around of 25 - 30 mins! Have aimed to keep it short and crisp!

The outline of the talk is as follows:

  • Introduction: Generative Modelling and its relation to GAN ( 5 min)
  • GAN - Application, and Components (4 min)
  • Training of GAN.(4 min)
  • Loss Function(4 min)
  • Challenges. (3 min)
  • Types and future of GAN. (5 min)
  • Questions and Answers! (5 min)


The talk is best suited for Beginners and for all those curious minds who like to know the very insights of every small thing. Modeling of new data has traveled a long distance and is highly celebrated as the field to find new methods and ways. The talk would help to create new enthusiasm for Machine Learning for all the beginners and would create a new path of exploration.

Video URL:

Speaker Info:

My name is Moinak Bose and to quote Sir Albert Einstein, I am "passionately curious". Although curiosity kills the cat, yet for me, it has led my road to explore from Maths to Machine Learning. The joys of Technology and Mathematics are quite unique and satisfying things to deal with. As a research scholar, I have published two of my most crucial works at reputed conferences. While pursuing my undergraduate degree, I also want to be the one who contributes to making whatever-is-outside-the-window a better place and that’s what brings me here, to learn, to explore, to deliver.

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Beginner
Last Updated: