Optional Technical Overview

Main Point

Follow the model’s numerical mechanism so you can distinguish a trained-model change from a context, generation-setting, resource, or application change.

Key Points

• Text is represented as tokens and vectors before learned transformations produce next-token scores.
• Training, inference, decoding and application validation perform different jobs.
• Small calculations make assumptions inspectable; they are classroom constructions.
• A useful comparison fixes the task and records which factor changed.

1. Your route through the course

Lessons 1–8 establish representations, attention and architecture. Lessons 9–12 connect the training signal to generation and context. Lessons 13–15 examine expert routing and compute tradeoffs. Lessons 16–18 connect instructions, uncertainty and controlled experiments to practical choices. The same request path will gain detail as each relationship becomes understandable.

2. Start with a concrete comparison

A museum needs a notice summary and accurate opening-hours fields. A changed checkpoint, a supplied notice and an added timetable tool can each change the resulting service. Record them separately before crediting an output difference to the model. No experiment has been run in this orientation.

3. Prerequisite refresh and optional depth

A vector is an ordered list of numerical coordinates. Training adjusts learned values; inference computes for an input using a chosen checkpoint. Runtime memory includes weights and working state. The lessons refresh these ideas when needed. Follow the main mechanism first, then use the optional arithmetic walkthroughs to check normalization, units and assumptions. Programming is optional for the initial paper-based project.

4. Materials and course artifact

Use the blank worksheets before opening their separate keys. Keep the mechanism diagram, calculation sheet, 10-case packet, experiment record and bounded interpretation together. Expected behavior is defined before a run. Observation fields stay blank until an actual run occurs. A later paid call requires a chosen approved access path and budget; understanding the course does not require a purchase.

5. Begin

Write 1 task you want a model to perform and 1 failure you would need to detect. Lesson 1 will help you define what system you are actually comparing.

Sources

This orientation previews the course’s taught mechanism and fictional museum task. Primary evidence, dates, methods and limits accompany the relevant lessons; the orientation makes no empirical model-performance claim.