• Benefits and possibilities of Generative AI
    • Understanding the battle between generator and discriminator
    • Understanding Cross Entropy in depth
    • Understanding the equation to calculate the discriminator loss
    • Understanding the equation to calculate the generator loss
  • GAN’s – Coding
    • Coding: importing libraries and declaring a visualization function
    • Coding: hyperparameters and the DataLoader
    • Coding: the generator class
    • Coding: the discriminator class
  • Coding an advanced generative architecture
    • Challenges and issues of the basic GAN
    • The Wasserstein Loss
    • The Gradient Penalty
    • Coding: setting up libraries and parameters
    • Coding: Login and setup of the Wandb stats library
    • Coding: Beginning the generator
    • Coding: Understanding convolutions
  • Generating images from text by combining two advanced architectures
    • Multimodal generation, an incredible adventure
    • Coding: importing the libraries
    • Coding: helper functions and hyperparameters
    • Coding: Setting up the CLIP model
    • Coding: Setting up the Generative transformer model
    • Coding: Setting up the latent space parameters to be optimized