Generative Adversarial Networks (GANs)
Watch the adversarial game unfold: the Generator creates fake data to match a target distribution while the Discriminator tries to distinguish real from fake. Try different target shapes, adjust training speed, and track KL divergence as the distributions converge.
Learn Generative Adversarial Networks (GANs) on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
Introduction to Generative AI Concepts
Understand the theory behind GANs, VAEs, and diffusion models. Learn how Generator and Discriminator networks compete to create realistic data.
Introduction to LLMs in Python
Explore how Large Language Models power modern generative AI tools. Learn to build and deploy text generation pipelines at scale.
Introduction to Deep Learning with PyTorch
Master PyTorch fundamentals—the framework of choice for implementing GAN training loops and custom loss functions.
Intermediate Deep Learning with PyTorch
Build advanced PyTorch architectures including Generator and Discriminator networks for image synthesis tasks.
Image Processing in Python
Learn image manipulation and processing techniques—essential for evaluating GAN output quality and preparing training data.
AI Fundamentals
A complete pathway through generative AI: LLMs, image generation, GANs, diffusion models, and real-world deployment.
