All the articles with the tag "machine-learning".
A complete derivation of backpropagation for MLPs — from chain rule intuition to delta propagation, with a worked numerical example showing exactly how errors flow backward through a network.
The evolution of image diffusion architectures. Learn how we moved from convolutional U-Nets to scalable Diffusion Transformers (DiT), and why treating images like language changed everything.
How to move from visual imitation to law-governed motion. Deep dive into injecting PDEs into neural networks, implicit physics extraction, and LLM-guided physical reasoning.
How to accelerate diffusion sampling and steer creativity. Learn the mechanics of DDIM, DPM-Solver, Classifier-Free Guidance (CFG), and the math of negative prompting.
A deep dive into how datasets and dataloaders power modern AI. Understanding the architectural shift from Python row-loops to C++ zero-copy data pumps.
A guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to 3D parallelism and fault tolerance.