Generative Adversarial Networks (GANs) have generators and discriminators, which allows the researcher to generate more data. It is a class of machine learning designed by Ian Goodfellow and his colleagues in 2014. GANs are used in various applications today and considered as one of the top domains in future AI Applications.

With an increasing demand for professionals to have a hand on experience of the GANs technology, we are bringing an extensive workshop which will allow the developers to learn and implement their own (GANs) from Scratch.

Format: Pre-recorded Videos (More than 5hours) & 5 Colab Notebooks
Pricing: $19.99 (workshop is free for ADaSci members)


01. Introduction to GANs

  • What are Generative Adversarial Networks (GANs)
  • How do GANs Work
  • Importance of GANs
  • GANs in Real-life
  • GANs applications in Industry
  • GANs for Privacy
  • GANs for Anonymity

02. The Theory of GANs

  • Overview of GANs’ algorithms
  • Generative algorithms
  • Generators
  • Discriminators
  • Wasserstein Loss Function
  • Variational autoencoders

03. Building a Vanilla GAN model:

  • Overview of CNN modules 
  • Defining generator module 
  • Defining discriminator module 
  • Building the GAN model
  • Loading data
  • Training the data 
  • Analysing output
  • Summary 

04. Challenges in GANs

  • Oscillating Loss
  • Mode Collapse
  • Uninformative Loss
  • Hyperparameters
  • Tackling the GAN Challenges

05. Play with GANs

  • DCGANs
  • GauGANs
  • Cycle GANs
  • Pix2pix GANs
  • StyleGANs
  • LSGANs Least Square GANs
  • CGANs Conditional GANs
  • CoGANs Couple GANs
  • SRGANs Super Resolution GANs


Learning Outcome

  • GANs from zero to one 
  • Understanding of DeepFakes
  • Hands-on knowledge of implementing GANs
  • Strong fundamentals for developing any GANs based application



  • Basic to Moderate level Python
  • Computer Vision using Deep Learning 
  • Familiarity with Google Colab and GPU environment 


Required Tools

  • Any editor to run the Python programs (preferably Google Colab Notebooks)
  • If working on an editor, TensorFlow, Pytorch and Keras must be installed
  • High-speed internet connection

Download the complete workshop at just $19.99


After payment, you will receive a confirmation email with the download link.


The workshop is free for ADaSci Members & CDS Charterholders.


    Is this workshop held online on 28th Nov, 2020?

  2. Joydeep Ghatak 3 months ago

    If I register for the program or apply for the membership, am I going to watch the recorded workshop videos online?
    The reason behind, I am in a different timezone than the workshop timezone.

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