Ongoing series
Each one is a single argument unfolded chapter by chapter.
Bailian model platform: prompt engineering, fine-tuning, agents, and evaluation.
Production-grade ML on Alibaba Cloud — DSW, DLC, EAS, Designer, QuickStart, end-to-end.
Six-piece field guide to Claude Code — config, modes, slash commands, MCP, hooks, SDK + GitHub.
Infrastructure, networking, and the platforms ML actually runs on.
OS, networking, compilers — the substrate beneath everything.
Algorithms by pattern, with worked solutions.
The geometry and computation that underlies all of ML.
Practical Linux — shell, processes, networking, and performance.
Deriving the algorithms — no hand-waving.
Modern NLP — language models, embeddings, transformers, and beyond.
From classical ODE methods to neural ODEs.
Self-hosted AI agent gateway from zero to a real working stack — install, channels, skills, MCP.
PINNs, neural operators, and the math behind learned PDE solvers.
From classic CF to GNN, GCSAN, HCGR, and modern multi-objective ranking.
Foundations of RL: MDPs, policy gradients, actor-critic, and offline RL.
Building infrastructure-as-code agents: planning, validation, and apply loops.
Statistical and deep methods for forecasting at scale.
Domain adaptation, fine-tuning, and representation transfer.