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AI Workshop: Hands-on with GANs with Deep Convolutional Networks

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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 (DCGANs). 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.

This course was created by Loonycorn. We are pleased to host this content in our library.


01 - Introduction
  • 01 - A quick overview of GANs

  • 02 - 1. Understanding Convolutional and Pooling Layers
  • 01 - Course outline and prerequisites
  • 02 - Setting up Google Colab cloud-hosted notebooks
  • 03 - Understanding convolutional neural networks
  • 04 - Transforming a multichannel image to tensor
  • 05 - Applying convolutional and pooling layers
  • 06 - Viewing the effect of different filters

  • 03 - 3. Training a Discriminator as a Classification Model
  • 01 - Types of convolutional layers
  • 02 - Training data for discriminator bad fakes and real images
  • 03 - Loading and transforming training image data
  • 04 - Understanding the discriminator architecture
  • 05 - Training a discriminator on bad fakes
  • 06 - Training data for discriminator good fakes and real images
  • 07 - Training a discriminator on good fakes

  • 04 - 3. Training a Deep Convolutional GAN
  • 01 - Generator and discriminator
  • 02 - Deep convolutional GANs (DCGANs)
  • 03 - Setting up data for GAN training
  • 04 - Setting up the generator and discriminator
  • 05 - Output from an untrained generator and discriminator
  • 06 - Setting up the GAN training loop
  • 07 - Viewing GAN training results

  • 05 - Conclusion
  • 01 - Summary and next steps
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