AI Image Generation with Nvidia & PyTorch
Exploring generative AI techniques using PyTorch and Nvidia’s deep learning tools. ✅ Trained a GAN from scratch on the MNIST dataset ✅ Explored embedding models for better representations ✅ Optimized training with Nvidia GPU acceleration
Problem & Solution
Why I Built This
"How do AI models generate images? Why do GANs work? And why is it that every time I start training, the first results look like nightmare fuel?" Most AI image generation applications focus on fine-tuning pre-trained models, but this project aimed to understand the fundamentals from scratch—not just running code, but truly grasping why and how these models generate realistic images.
How It Solves It
By implementing a GAN from scratch, I learned the step-by-step process of how an AI can create realistic images, from training the generator & discriminator networks to stabilizing loss functions and batch updates.
See It In Action
Impact & Future Improvements
What This Achieved
I built an AI that can generate images from random noise—and I finally understand why it works while also gaining hands-on experience with PyTorch, CUDA acceleration, and GAN mechanics.
Possible Improvements
See FAQ