Latent Diffusion Models for MNIST

Built an unconditional latent diffusion model on MNIST.
- Autoencoders + Channel Attention Blocks
- U-Net DDPM for denoising in latent space
- t-SNE embeddings & reconstruction quality

Built an unconditional latent diffusion model on MNIST.

Real-time hand-gesture detection powering a fully automated game with cheating detection and dynamic winner celebration.

Comparing reconstructions across latent sizes to illustrate the compression–fidelity trade-off.

Gaussian & salt-and-pepper noise removal via Attention U-Net + PatchGAN, achieving strong PSNR/SSIM.

Hands-on study of classic RL algorithms with Boltzmann exploration tweaks.

Multi-label classifier trained on 44k images for real-time tagging and dataset organization.

CNN-based app (Flask + JS) for accurate real-time recognition of Iranian celebrities.

LLaMA-3 + CrewAI pipeline for consistent, automated repository documentation.

Hands-on survey of supervised learning techniques on diverse datasets.