AI Workshop: Hands-on with GANs Using Dense Neural Networks

Introduction

• Understanding generative modeling

1. Introducing Generative Adversarial Networks

• Course outline and prerequisites
• Set up the virtual environment and run the notebook server
• Introducing generative adversarial networks (GANs)
• Instantiating the dataset and data loader
• Viewing training data

2. Stand-Alone Training of Adversaries

• Big picture overview of a GAN
• Training the adversaries
• The generator architecture and discriminator architecture
• Understanding the generator and discriminator outputs
• Stand-alone training of discriminator as classification model
• Stand-alone training of generator

3. Training Generative Adversarial Networks

• Computing losses for generator and discriminator
• Understanding the minimax loss function
• Setting up GAN training
• Visualizing GAN training results
• Problems with GANs and potential mitigations

Conclusion

• Summary and next steps