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Generative Adversarial Networks (GAN) - Essentials

( Duration: 2 Days )

Generative models are gaining a lot of popularity recently among data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labelled data. In GAN, a generator function learns to synthesize samples that best resemble some dataset, while a discriminator function learns to distinguish between samples drawn from the dataset and samples synthesized by the generator. GANs have emerged as a promising framework for unsupervised learning: GAN generators are able to produce images of unprecedented visual quality, while GAN discriminators learn features with rich semantics that lead to state-of-the-art semi-supervised learning.

In Generative Adversarial Networks (GAN) - Essentials training course, you will learn to have a basic understanding of Generative Adversarial Networks (GAN) and its applications.

By attending Generative Adversarial Networks (GAN) - Essentials workshop, delegates will learn:

  • Image Generation with Variational Autoencoder
  • Image Transformation with Neural Style Transfer and Deep Dream
  • Implementation of DC GAN with Tensorflow 2.x
  • Conditional GAN and Cycle GAN

  • Python
  • Keras
  • Machine Learning
  • AI Developers
  • Artificial Intelligence Engineers
  • Data Scientists



Overview of Generative Models

  • What is Generative Models
  • Application of Generative Models
  • Types of Generative Models
  • Magenta Sketch-RNN

Deep Dream

  • What is Deep Dream?
  • DeepDream Applications
  • How Deep Dream Work?
  • Implementation of Deep Dream on Tensorflow 2.x

Neural Style Transfer

  • What is Neural Style Transfer?
  • Fast Style Transfer using TF-Hub
  • Implementation of Neural Style Transfer on Tensorflow 2.x

Variational Autoencoder (VAE)

  • What is Autoencoder
  • Variational Autoencoder (VAE)
  • VAE Implementation on Tensorflow 2.x

Generative Adversarial Networks (GAN)

  • Introduction to GAN?
  • GAN Applications
  • Basic DC GAN Architecture
  • Discriminator and Generator Loss
  • DCGAN Implementation on Tensorflow 2.x
  • GAN Challenges and Tricks

Conditional GAN (C-GAN)

  • Introduction to C-GAN
  • Image to Image Translation & Pix2Pix
  • Pix2Pix implementation on Tensorflow 2.x


  • Introduction to CycleGAN
  • Cycle Consistency Loss
  • CycleGAN implementation on Tensorflow 2.x

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