Build Basic Generative Adversarial Networks (GANs)

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  • Week 1: Intro to GANs
    • See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
  • Week 2: Deep Convolutional GANs
    • Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!
  • Week 3: Wasserstein GANs with Gradient Penalty
    • Learn advanced techniques to reduce instances of GAN failure due to imbalances between the generator and discriminator! Implement a WGAN to mitigate unstable training and mode collapse using W-Loss and Lipschitz Continuity enforcement.
  • Week 4: Conditional GAN & Controllable Generation
    • 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!