AI Workshop: Hands-on with GANs using Dense Neural Networks
1h 32mIntermediate2025-09-23
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. Janani also discusses how to address common problems associated with GANs and mitigate them effectively throughout the training process.
Skills covered
Traditional AI and Machine LearningArtificial Intelligence (AI)One-Off
Concepts
Introducing Generative Modeling
- Understanding generative modeling
Introducing Generative Adversarial Networks (GANs)
- Course outline and prerequisites
- Set up the virtual environment and run the notebook server
- Introducing 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
- The discriminator architecture
- Understanding the generator and discriminator outputs
- Stand-alone training of a discriminator as a classification model
- Stand-alone training of a generator
Training GANs
- Computing losses for generators and discriminators
- Understanding the minimax loss function
- Setting up GAN training
- Visualizing GAN training results
- Problems with GANs and potential mitigations
Conclusion
- Summary and next steps