Description
Enter the Age of Generative AI: Create, Not Just Predict
Most machine learning analyzes data—but Generative AI creates it. This course teaches you two revolutionary architectures: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). You’ll build systems that generate faces, art, and synthetic data—skills in high demand in gaming, fashion, healthcare, and research.
Projects You’ll Build
- Face generator that creates photorealistic—but fake—human faces.
- AI art studio that blends styles like Van Gogh and Picasso.
- Data synthesizer that generates privacy-safe medical records for research.
- Image-to-image translator that turns sketches into colored illustrations.
Key Concepts Covered
- GAN architecture—generator vs. discriminator, adversarial training
- VAE theory—latent space, reparameterization trick, reconstruction loss
- Training stability—dealing with mode collapse and vanishing gradients
- Evaluation metrics—Inception Score, FID, visual inspection
- Modern variants—DCGAN, CycleGAN, Age-cGAN
Why Generative AI?
- Explosive industry demand—from NVIDIA to Adobe, generative models are core to new products.
- Research frontier—VAEs and GANs underpin diffusion models like DALL·E and Stable Diffusion.
- Portfolio power—a GAN project instantly sets you apart in job interviews.
Who Is This For?
- Deep learning practitioners ready for advanced topics
- Computer vision engineers expanding into synthesis
- Researchers needing generative baselines
- Creative coders building AI art tools
From Noise to Masterpiece
You’ll start with random noise—and end with systems that fool humans. This course balances theory (KL divergence, Nash equilibrium) with hands-on TensorFlow code, so you understand why it works, not just how.
Don’t just use generative AI—build it. Enroll today.
