graykode/nlp-roadmap: a mind map and keyword list for learning NLP

What graykode/nlp-roadmap actually is

The repository describes itself as a ROADMAP (Mind Map) and KEYWORD list for students interested in learning Natural Language Processing, covering material from basic probability and statistics to SOTA NLP models. That description is accurate about scope and honest about format. The top level of the repository holds four entries: LICENSE, README.md, data/ and img/. There is no source tree, no build file, no test suite. The README's curriculum is a numbered list of four anchors (Probability and Statistics, Machine Learning, Text Mining, Natural Language Processing), and each of those sections in the README is empty. The content lives in the images and in the data directory.

Who this is for: a student who already knows they want to work on language data and wants a map of the territory before choosing a textbook. It is also usable by an instructor assembling a syllabus, because the keyword boxes can be read as a checklist of topics. It is not for someone who wants to run a model this afternoon. Nothing here executes.

The mind-map format and why the README warns about it

The project is built on semantic mind maps. Keywords are placed in boxes and connected by lines, and the README is explicit that this is a weakness as well as a style: the relationship among keywords could be interpreted in ambiguous ways, and readers should focus on the keyword in the square box and treat those as the essential parts to learn. That is an unusual admission, and it is the right one. A line between two nodes in a mind map can mean prerequisite, subfield, commonly-used-together, or simply that the author ran out of space in that direction. The README does not define an edge vocabulary, so the reader supplies the meaning.

The format also constrains density. The README notes that containing a plethora of keywords and knowledge within just an image has been challenging, and that the roadmap is one suggestion or idea rather than a definitive ordering. Practically, that means the images are best read as a vocabulary list with spatial grouping, not as a dependency graph. If you want to know what to learn first, the map will not tell you reliably; it will tell you what exists.

Installing and using graykode/nlp-roadmap

There is nothing to install. The repository has no package manifest, no setup instructions and no executable code, so the only meaningful first use is cloning it and opening the images. The README points readers at the repository itself for the curriculum.

Clone the repository and list what you actually get:

git clone https://github.com/graykode/nlp-roadmap.git
cd nlp-roadmap
ls

You should see LICENSE, README.md, data/ and img/. The images are what the README calls the mind maps; the data directory holds the keyword material. Open the image directory to view them:

ls img/

The README does not document image formats, resolutions or filenames, so treat the listing as the source of truth. If you plan to annotate the map for your own study, copy an image out and mark it up locally rather than editing in place, because the repository has no build step that would regenerate anything.

Contributions follow the same guide as kamranahmedse/developer-roadmap, according to the README, and the author states that everyone can contribute, from fixing typos to offering different perspectives on the material. If you want to add a keyword, that is the route.

Where the roadmap stops being useful

The largest limitation is that the repository is a map without a territory. Four curriculum headings in the README contain no prose. There is no explanation of what a keyword means, no citation next to individual keywords, and no ordering beyond the four top-level categories. A reader who does not already know what, say, a conditional random field is will not learn it here; they will learn that the term exists and that it sits somewhere near sequence labelling.

Second, the ambiguity the README admits is not a minor caveat. If you use the map to decide what to study next, you are reading edges that were not defined. That is a real failure mode for self-directed learners, who are exactly the stated audience.

Third, the project is a snapshot of the field at the time the images were drawn. The README cites Bishop (2006) and a 2017 arXiv paper by Young, Hazarika, Poria and Cambria among its references, and the copyright line reads 2019. Nothing in the repository describes a process for refreshing the maps as the field moves. Whether the keyword set still matches current practice is something you have to judge yourself against other sources, because the repository offers no changelog and no release history.

How it compares with other roadmap repositories

The README points contributors to kamranahmedse/developer-roadmap for its contribution guide, which invites the obvious comparison. That project is a web application: the roadmaps are rendered from structured data, they are navigable in a browser, and they are updated continuously through a large contributor base. graykode/nlp-roadmap is a set of static images in an img/ directory. The difference in approach is not cosmetic. Structured data can be searched, filtered, translated and diffed; an image cannot. If you want a roadmap you can link into a syllabus, annotate, or query for a keyword, the data-driven approach wins on mechanics.

The trade-off runs the other way too. A single dense image communicates the shape of a field at a glance in a way that a scrollable web page does not, and it survives being printed. The README's own framing, that this is one suggestion or idea, fits that use. For a reading group that wants one poster on the wall, the image format is the point. For a team maintaining a curriculum across years, it is the wrong tool.

Two other references in the README are worth naming as alternatives for the learning material itself: ratsgo's blog for text mining and lovit's textmining-tutorial, both cited by the author. Those are prose and code; this repository is neither.

Licence, reuse and the cost of keeping it current

The repository is MIT licensed, copyright 2019 Tae-Hwan Jung. MIT permits commercial use and modification. The README goes further than the licence text and asks that users of the material leave a reference, describing that as highly expected rather than required. That is a request, not an added licence term, and it does not change what MIT allows. If you redistribute the images or build them into teaching material, keeping the attribution is the courteous reading of the author's note; whether you are obliged to is a question for your own counsel, not for this article.

The upgrade cost is unusual for an open source project because there is no dependency graph to maintain. You will not be patching a library. What you will be doing is deciding, periodically, whether the maps still reflect the field. That decision has no tooling behind it: no version number, no release notes, no diff. No last push date is recorded in the repository listing, so there is no basis for a claim about how actively it is maintained. Judge freshness by opening the images and comparing them against what you already know.

Editorial conclusion

Adopt graykode/nlp-roadmap if you want a visual index of NLP topics to plan your own reading, or if you teach and need a keyword checklist to hand out. Do not adopt it if you need exercises, code samples, or an ordered path with prerequisites, because the repository is images plus a README and the README states the relationship between keywords is ambiguous and the roadmap is only one suggestion. Before relying on it, open the img/ directory and confirm the curriculum images actually render at a readable size, then check the data/ directory for the keyword lists, since those two directories are the entire content of the project.

Frequently asked questions

Official sources

1. Official documentation
2. Official README
3. Project repository