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Course Outline
Introduction
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
GANs
- 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
Requirements
- Python programming experience
Audience
- Data Scientists
14 Hours
