AI Workshop: Hands-on with GANs using Deep Convolution Networks
1h 47mIntermediate2025-08-29
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 deep convolutional generative adversarial networks (GANs). Explore the core components of convolutional and pooling layers, including setting up Google Colab cloud-hosted notebooks, transforming multichannel images to tensors, applying layers, and viewing filter effects. Janani covers the basics of training a discriminator as a classification model and training a deep convolutional GAN like a pro, from setting up data for GAN training, setting up the generator and discriminator, and outputting from an untrained generator and discriminator to creating a training loop, viewing and evaluating results, and more.
Skills covered
Neural Networks and Deep LearningGenerative AIArtificial Intelligence FoundationsArtificial Intelligence (AI)One-Off
Concepts
0. Introduction
- 01 - Overview of generative adversarial networks (GANs)
1. Understanding Convolutional and Pooling Layers
- 02 - Course outline and prerequisites
- 03 - Setting up Google Colab cloud-hosted notebooks
- 04 - Understanding convolutional neural networks
- 05 - Transforming multichannel image to tensor
- 06 - Applying convolutional and pooling layers
- 07 - Viewing the effect of different filters
2. Training a Discriminator as a Classification Model
- 08 - Types of convolutional layers
- 09 - Training data for discriminator bad fakes and real images
- 10 - Loading and transforming training image data
- 11 - Understanding the discriminator architecture
- 12 - Training a discriminator on bad fakes
- 13 - Training data for discriminator good fakes and real images
- 14 - Training a discriminator on good fakes
3. Training a Deep Convolutional GAN
- 15 - Generator and discriminator
- 16 - Deep convolutional GANs (DCGANs)
- 17 - Setting up data for GAN training
- 18 - Setting up the generator and discriminator
- 19 - Examining the ouput from an untrained generator and discriminator
- 20 - Setting up the GAN training loop
- 21 - Viewing GAN training results
Conclusion
- 22 - Summary and next steps