Char-level, trained on a corpus you choose. This lesson sits inside Module I — From scratch — of Transformers (Applied), the course that anchors the Applied Machine Learning program. It is not a survey; it is the specific, working understanding of "Lab: Build a small transformer" that the rest of the course assumes you carry forward.
- 01Define Lab: Build a small transformer in the precise sense used across Transformers (Applied).
- 02Recognize when Lab: Build a small transformer is the correct lens for the situation in front of you, and when it is not.
- 03Apply Lab: Build a small transformer to a concrete case drawn from From scratch, and defend the result in plain language.
- 04Connect Lab: Build a small transformer to the adjacent lessons in this module without collapsing the distinctions between them.
The idea, stated plainly
Char-level, trained on a corpus you choose. That single sentence is the whole lesson in compressed form. The rest of the reading unfolds it — what it means when the terms are taken seriously, where it comes from, and what work it does inside Transformers (Applied). Read the sentence, then read it again after the sections below; it should carry more weight the second time.
Why it belongs in From scratch
Module I exists because implementing the block. "Lab: Build a small transformer" is one of the pillars of that module: without it, the later lessons either become memorization or lose their bite. Notice which earlier lessons this one leans on, and which later lessons will lean on it — the shape of the module is easier to see once you place this piece.
How the School of Artificial Intelligence faculty use it
In practice, working school of artificial intelligence professionals reach for this idea before they reach for a formula or a tool. It is a way of framing the problem so that the right question comes first. The mark of understanding is not that you can recite Lab: Build a small transformer; it is that you catch yourself using it, unprompted, when the situation calls for it.
Common misreadings
The most frequent error is to treat Lab: Build a small transformer as a slogan and skip the mechanics. The second most frequent is the opposite — treating the mechanics as the point, when the mechanics are only there to make the idea usable. Both errors collapse the same distinction, and both are correctable by returning to the one-line summary and asking what it actually claims.
- Lab: Build a small transformer is a working tool, not a slogan.
- Its meaning is set by the module it lives in: From scratch.
- Understanding is demonstrated by unprompted use in the correct situation.
- The adjacent lessons in this module are its natural context; read them together.
Take one real situation from your own life or from a public case study, and analyze it through the lens of Lab: Build a small transformer. Write it up as a short institutional memo — no jargon, no hedging — so that a colleague reading it in five years still understands the situation and the reasoning.
Deliverables
- — A one-paragraph statement of the situation as you found it.
- — A structured analysis applying Lab: Build a small transformer to that situation.
- — A concrete recommendation, with the reasoning made explicit.
- — A short "what I would change if I were wrong" section.
Rubric
- — Fidelity — the situation is described accurately, without editorial gloss.
- — Correct application — the lens is used in the sense taught in Transformers (Applied).
- — Clarity — the memo reads cleanly to someone outside the course.
- — Intellectual honesty — the analysis names its own assumptions and limits.
- 310 — Transformers (Applied), Module I: From scratch — The parent module for this lesson. Re-read the module blurb after finishing the lesson.
- The Anabasis Academy — School of Artificial Intelligence, Applied Machine Learning — The wider program this lesson serves; the Certificate in Applied ML (Practitioner tier). credential ultimately certifies mastery of ideas like this one.