# MachineLearning_notes: A Curated Link Index, Not a Library

> mdozmorov/MachineLearning_notes is an MIT-licensed Markdown index of machine learning, deep learning and data analysis resources. It is a reading list maintained by hand, and its value depends entirely on whether the links it points to still exist.

**mdozmorov/MachineLearning_notes** — Machine learning and deep learning resources

- Repository: https://github.com/mdozmorov/MachineLearning_notes
- Stars: 574 · Forks: 91
- Language: Unknown
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/mdozmorov-machinelearning-notes

## A Markdown Index Rather Than an Installable Package

The repository describes itself as "Machine and deep learning and data analysis resources." That sentence is the whole product. There is no setup script, no package manifest, no importable module and no command-line entry point in the material supplied. What a reader gets is a set of Markdown files, chiefly a README that functions as a link directory, plus a CONTRIBUTING.md file that the README links to for people who want to add entries. The primary language field is listed as unknown, which fits a repository whose contents are prose and URLs rather than source code. The intended reader is someone who wants a starting point for a topic and is willing to follow links outward, not someone who wants to run a model today. Treating it as a software dependency will lead to confusion, because there is nothing to install.

## What the Table of Contents Actually Covers

The README's table of contents splits the material into two large halves. The deep learning half contains sections for Cheatsheets, Awesome Deep Learning, then Keras and Tensorflow, PyTorch, JAX, Graph Neural Networks and Transformers, followed by DL Books, DL Courses & Tutorials, DL Videos, DL Papers (with a DL Genomics subsection), DL Tools (Auto ML, DL models, DL projects, and under DL projects: Language models, Audio/music/voice, Image/video/vision) and DL Misc. The machine learning half is smaller: Awesome Machine learning, ML Books, ML Courses & Tutorials, ML Videos, ML Papers, ML Tools and ML Misc. The imbalance is visible in the heading list itself: the deep learning branch has nested subsections three levels deep, while the ML branch stops at one level. If your interest is classical ML rather than neural networks, the coverage here is thinner by structure, not just by accident.

## The doctoc Marker Comments and What They Imply

The table of contents sits between two HTML comment markers. The opening one reads "START doctoc generated TOC please keep comment here to allow auto update", the closing one reads "END doctoc generated TOC please keep comment here to allow auto update", and between them a second comment states "DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE". This tells you the maintainer regenerates the table of contents with doctoc rather than editing it by hand. The practical consequence for a contributor is that adding a heading without running doctoc leaves the table of contents stale, and editing the list manually will be overwritten the next time the tool runs. It also means the repository has a tooling dependency that is not declared anywhere in the material supplied; there is no package.json or requirements file visible. Anyone forking this to build a personal index should either keep the doctoc workflow or delete the marker comments so future edits are not silently reverted.

## The Entries Are Pointers, and Pointers Rot

Every item follows the same shape: a bracketed name, a URL, a hyphen, and a short description. Examples from the README include a link to cheatsheets-ai described as "Essential Cheat Sheets for deep learning and machine learning researchers", and a link to stanford-cs-229-machine-learning described as "VIP cheatsheets for Stanford's CS 229 Machine Learning" with a separate online version URL. Some entries carry extra provenance, such as a source tweet or a Medium post. None of this is verified by anything in the repository. There is no link checker, no CI configuration shown in the material, and no scheduled job mentioned. A curated list of this kind degrades as the web changes underneath it, and the only defense is manual review. The last push timestamp is recent, which suggests the maintainer is still active, but activity on the index does not tell you which individual links were rechecked. Assume nothing about a URL until you open it.

## Contributing and the Cost of Keeping It Current

The README invites contributions twice: "Please, contribute and get in touch" with a link to CONTRIBUTING.md, and a PRs-welcome badge in the header. The maintenance model is therefore pull requests against a Markdown file, reviewed by a human. That is cheap to run and slow to scale. There is no versioning scheme, no release history (the material notes that no releases were retrieved) and no changelog, so a consumer cannot tell what changed between two points in time except by reading the commit log directly. If you vendor this list into your own documentation, you inherit the maintenance burden: you become responsible for the dead links. For a personal bookmark file that is fine. For anything that other people depend on, the absence of a release process is a real constraint, because there is no stable identifier to pin against.

## Where an Awesome List Does the Job Better

The obvious alternative is a single-topic awesome list, such as the awesome-deep-learning and awesome-computer-vision repositories that this README itself links to. The difference in approach is scope versus depth. Those lists commit to one subject and typically carry a code of conduct, a contribution guide and a stated inclusion policy for that subject alone. MachineLearning_notes spans cheatsheets, books, courses, videos, papers and tools across two broad fields, which makes it a good first stop when you do not yet know which subfield you need. It is a worse second stop. Once you know you care about graph neural networks specifically, a dedicated list will have more entries and a narrower review standard. The trade-off is real and the README does not hide it: it points outward to the specialized lists rather than trying to absorb them.

## Licence Terms and What MIT Covers Here

The repository is MIT licensed, indicated by the badge in the README header linking to opensource.org. MIT is permissive: it allows reuse, modification and redistribution provided the copyright notice and permission notice are retained. For a collection of links, the licence applies to the compilation and any original descriptive text, not to the third-party material the links point to. Copying the list into your own project is permitted under MIT; copying the content behind the links is governed by each destination's own terms, which the repository does not track. This is a description of the stated licence, not legal advice, and anyone redistributing the list commercially should read the LICENSE file in the repository rather than relying on the badge.

## How to Use It Without Installing Anything

Because there is no build step, the workflow is git and a text editor. Clone the repository, then search it locally: git clone https://github.com/mdozmorov/MachineLearning_notes and grep -n -i "transformer" README.md will list every entry mentioning the term with its line number. To add a resource, append a bullet under the matching heading in the README, keep the bracketed-name, URL, hyphen, description format, then regenerate the table of contents with doctoc so the marker comments stay accurate. If you would rather not run doctoc, delete the two marker comments and the warning between them, and maintain the list by hand from then on. If you plan to send a pull request, read CONTRIBUTING.md first, since the README directs contributors there and the material does not reproduce its contents.

## Conclusion

Adopt this repository if you want a single Markdown file of ML and DL links you can clone, grep and edit, and if you are willing to verify each link yourself before using it. Do not adopt it if you need installable code, pinned dependency versions, or a guarantee that any listed resource is still online; nothing in the repository enforces that. Before relying on it, check the section you care about against the live URL, confirm the doctoc marker comments are still present so your own edits do not break the table of contents, and read CONTRIBUTING.md to see what the maintainer expects from a pull request.

## FAQ

### what is machine learning notes

A MIT-licensed curated index of external machine and deep learning resources rather than notes of its own. The top level of the repository is a licence, a contributing guide, a gitignore, the README, and one prompt-command cheat sheet, and there are no releases or tags, so its currency comes from commits rather than versions.

### What is machine learning in short notes?

The cheat sheets section of this index links to short machine learning summaries written by other people, including a thirty-page sheet with classical equations and diagrams, a five-page sheet focused on the most popular algorithms, a four-page data science sheet for exam and interview review, and teaching cheat sheets for a university machine learning course.

### Where can I find complete notes for machine learning?

This repository points at other people's material rather than writing its own. The deep learning half is organised by framework and then by format, with sections for books, courses and tutorials, videos, papers including a genomics subsection, tools, and a miscellaneous bucket, and the machine learning half repeats that structure.

### Can I learn ML in 3 months?

The repository makes no time claim, which is the honest answer. What it offers toward one is a cheat sheet section, a hundred-slide overview with an accompanying short video, a blog post collecting four hundred and fifty free online university courses across many subjects, and separate books, courses, and video sections in both halves of the index.

## Sources

- [Issues](https://github.com/mdozmorov/MachineLearning_notes/issues)
- [License: MIT](https://github.com/mdozmorov/MachineLearning_notes/blob/master/LICENSE)
- [mdozmorov/MachineLearning_notes on GitHub](https://github.com/mdozmorov/MachineLearning_notes)
- [README](https://github.com/mdozmorov/MachineLearning_notes/blob/master/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/mdozmorov-machinelearning-notes