All the articles with the tag "machine-learning".
A practitioner's guide to distributed training frameworks — the memory formulas, parallelism strategies, and failure-mode reasoning that ML infra interviews actually test. Covers DDP, FSDP, DeepSpeed ZeRO, 3D parallelism, and fault tolerance.
GPU utilization is a lagging indicator — the real battle is in the data pipeline. A practitioner's deep dive into PyTorch DataLoader internals, zero-copy data pumps, WebDataset streaming, and the exact questions this gets you in ML system design interviews.
Pre-training gives a model knowledge; post-training gives it behavior. A practitioner's breakdown of SFT, LoRA/QLoRA, DPO, and GRPO — with the memory math, concrete configs, and interview reasoning that separates candidates who've done this from candidates who've read about it.
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.