Tag: learning

This is a good visual refresher for the linear algebra that shows up everywhere in ML systems: vectors, matrices, transformations, projections, and the geometry behind model representations.

Keeping it because the best ML explanations usually become easier once the shapes feel physical instead of symbolic.

This is a softer link than the GPU and agent material, but it fits the site because visual thinking changes how technical ideas become explainable.

Useful as a reminder that diagrams are not decoration. They are often the shortest path from symbol manipulation to shared understanding.

This is a beginner resource, but it is worth keeping because feature, label, target, and ground truth confusion still causes real project mistakes.

Useful as a glossary-level companion to the ML terminology posts. The caveat is that the definitions are introductory, so they should support judgment rather than replace model-review rigor.

This is a practical reference link for the part of data science that happens before the model: selecting, filtering, grouping, reshaping, and checking tabular data.

I would not treat a cheat sheet as a learning path, but it is exactly the kind of link that saves context switching during exploratory analysis.

This visual guide is useful because embeddings become easier to reason about when they stop being abstract vectors and start feeling geometric. That matters for RAG, clustering, recommendations, search, and model evaluation.

I would keep it as a teaching aid, not as an implementation reference. The value is intuition: what similarity means, why representation quality matters, and why retrieval errors often start before the model answers.

This is a classic reference for understanding PostgreSQL below the SQL surface. Query processing, storage, concurrency, and transaction behavior become much less mysterious when the internals are visible.

Keeping the original http URL is intentional because that is the source that resolves. This is reference material, not a polished marketing page.

This is a good systems explanation for software developers who use databases every day but have not built a mental model of parsing, planning, storage, indexes, and execution.

Worth keeping because better database intuition changes how you design APIs, migrations, reports, and data products.

This is a broad reference, but it is still worth keeping because system design vocabulary helps developers talk across product, infrastructure, and data boundaries.

The caveat is that primers can encourage interview-shaped thinking. Use it to build a map, then ground decisions in the actual product constraints.