All the articles with the tag "machine-learning".
A reader-friendly guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to checkpoints, mixed precision, and fault tolerance.
A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.
The evolution of image diffusion models from U-Net architectures to Diffusion Transformers (DiT). Covers latent diffusion, the DiT revolution, and the complete image generation pipeline.
Deep dive into state-of-the-art video generation models: Sora, Veo 3, and Open-Sora. Plus motion modeling techniques using optical flow, geometry, and diffusion fields.
How video diffusion models are built through pre-training and aligned through post-training. Covers the billion-frame training problem, DPO, RLHF, and the complete training pipeline.
How to accelerate diffusion sampling and control output quality. Covers DDIM, DPM-Solver, Classifier-Free Guidance (CFG), negative prompting, and inference optimization techniques.