MLU-Explain

A collection of visual essays explaining selected machine-learning methods and evaluation ideas.

Plan for this page

Decide whether this external resource fits your goal before leaving the platform.

You will leave with: Topics include neural networks, regression, reinforcement learning, ROC/AUC and fairness measures, with scope varying by article.
Time: about 1 hr
Before you begin: Varies; many articles assume algebra, probability or basic machine-learning vocabulary.
Do this now: First read why it was selected, how to use it, and its main limitation.

MLU-ExplainTopics include neural networks, regression, reinforcement learning, ROC/AUC and fairness measures, with scope varying by article.

It offers focused concept explainers that can supplement, but not replace, a sequenced course.
Technical beginners through intermediate learners, depending on the essay.
Link to one reviewed essay at a time and add a local context note about prerequisites, date and what the visualization omits.

Topics include neural networks, regression, reinforcement learning, ROC/AUC and fairness measures, with scope varying by article.

Fit and purpose

Why selected

It offers focused concept explainers that can supplement, but not replace, a sequenced course.

Suitable for

Technical beginners through intermediate learners, depending on the essay.

Prerequisites

Varies; many articles assume algebra, probability or basic machine-learning vocabulary.

Use and access

How to use it

Link to one reviewed essay at a time and add a local context note about prerequisites, date and what the visualization omits.

Language

English.

Access

The public essays can be read without an account.

Accessibility

Interactive and visual elements were not tested for keyboard, contrast, reduced motion or screen-reader equivalence.

Interpretation and permissions

Perspective

Industry educational perspective from Amazon’s internal/external machine-learning training initiative.

Rights

Review licence and attribution for each essay and asset before reuse; link-only is the safe default.

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