Generative Adversarial Networks (GAN) Course for Beginners


Dive into the evolving world of AI and stay ahead with our course on Generative Adversarial Networks (GANs). GANs, a significant subset of generative models, have gained immense popularity among data scientists. Learn how to leverage GANs to construct AI systems that autonomously comprehend and interpret raw data without the need for labeled datasets, setting them apart from traditional supervised learning methods. Developed by Ian Goodfellow in 2014, GANs consist of a generator function that synthesizes samples resembling a dataset and a discriminator function that distinguishes between real and generated samples. Explore the capabilities of GANs for unsupervised learning, producing high-quality images and enabling state-of-the-art semi-supervised learning. This course provides a comprehensive understanding of the trending field of generative models, making it extremely relevant for anyone looking to upskill and advance their knowledge in AI and chatbot development, including applications like ChatGPT.

Course Objectives:

  • Generative model using Auto Encoder
  • GAN Architecture
  • GAN Implementation on MNIST dataset
  • Style Transfer with GAN
  • Image Generation with GAN


7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Overview of Generative Models
    1. What is Generative Models
    2. Application of Generative Models
    3. Types of Generative Models
    4. Online Generative Model Demo with Sketch-RNN
    5. Installing Google Colab
  2. DeepDream
    1. Recap on Convolutional Neural Networks (CNN)
    2. Recap on Transfer Learning
    3. What is DeepDream?
    4. DeepDream Applications
    5. DeepDream Implementation
  3. Neural Style Transfer
    1. What is Neural Style Transfer?
    2. Neural Style Transfer Applications
    3. Neural Style Transfer Implementation
  4. Variational Autoencoder (VAE)
    1. What is Autoencoder
    2. Variational Autoencoder (VAE)
    3. VAE Implementation
  5. Generative Adversarial Networks (GAN)
    1. What is GAN?
    2. GAN Applications
    3. Basic DCGAN Architecture
    4. DCGAN Implementation
    5. GAN Challenges and Tricks
  6. Text Generation
    1. Recap on Recurrent Neural Networks (RNN)
    2. Recap on Long Short Term Memory (LSTM)
    3. Char by Char Text Generation with LSTM