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AI Workshop: Hands-on with GANs Using Dense Neural Networks (2023)

1h 25mIntermediate2023-12-08

Authors

Janani Ravi

Certified Google Cloud Architect and Data Engineer

Course details

If you’re looking for hands-on AI practice, this workshop-style coding course was designed for you. Join instructor Janani Ravi as she shows you how to build and train generative adversarial networks (GANs). Explore the core components of GANs including how to set up the virtual environment, run the notebook server, instantiate the PyTorch Dataset, DataLoader, and more. Janani covers the basics of standalone training of adversaries, training GANs, and visualizing results. By the end of this course, you’ll also know how to address common problems associated with GANs and mitigate them effectively throughout the training process.

Skills covered

Neural Networks and Deep LearningGenerative AIArtificial Intelligence (AI)One-Off

Concepts

Introduction

Understanding generative modeling

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

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

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