coursera-gan-specialization | Programming assignments and quizzes from all courses | Machine Learning library

This is the first course of the Generative Adversarial Networks (GANs) Specialization.
Learn about GANs and their applications, understand the intuition behind the basic components of GANs, and build your very own GAN using PyTorch.
Assignment: Your First GAN
Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images.
Assignment: Deep Convolutional GAN (DCGAN)
Reduce instances of GANs failure due to imbalances between the generator and discriminator by learning advanced techniques such as WGANs to mitigate unstable training and mode collapse with a W-Loss and an understanding of Lipschitz Continuity.
Assignment: Wasserstein GAN with Gradient Penalty (WGAN-GP)
Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories.
Assignments: Build a Conditional GAN Controllable Generation
This is the second course of the Generative Adversarial Networks (GANs) Specialization.
Understand the challenges of evaluating GANs, learn about the advantages and disadvantages of different GAN performance measures, and implement the Fréchet Inception Distance (FID) method using embeddings to assess the accuracy of GANs.
Assignment: Evaluating GANs / Fréchet Inception Distance
Find out the disadvantages of GANs when compared to other generative models, discover the pros/cons of these models — plus, learn about the many places where bias in machine learning can come from, why it’s important, and an approach to identify it in GANs.
Quiz: Analyzing Bias
Assignment: Bias
Lab: Variational Autoencoder (VAE)
Understand how StyleGAN improves upon previous models and implement the components and the techniques associated with StyleGAN, currently the most state-of-the-art GAN with powerful capabilities
Assignment: Components of StyleGAN
Optional Notebooks: Components of BigGAN StyleGAN2