Course Outline


GANs and Variational Autoencoders

  • What is a GAN? What are variational autoencoders?
  • GAN and variational autoencoders architecture

Preparing the Development Environment

  • Instaling and configuring TensorFlow

Generative Models

  • Sampling data
  • Working with Bayes Classifier and Gaussian mixture model

Variational Autoencoders

  • Parameterizing and reparameterizing with neural networks
  • Finding dimensionality reduction
  • Visualizing latent space


  • Implementing backward propagation
  • Working with loss functions
  • Training a classifier model
  • Generating new data

Advanced GANs

  • Working with conditional GAN
  • Working with deep convolutional GAN
  • Working with progressive GAN

Summary and Conclusion


  • Python programming experience


  • Data Scientists
  14 Hours


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