Ryan Curtin
c8eb3dceac
* Add pipeline to documentation homepage. * Fix for mobile devices. * Add little pipelines to go at the top of each page. * Overhaul index page. * Overhaul README to remove redundant material. * Add installation documentation. * Update pipelines. * Allow nesting of deeper details. * Add a pipeline to the top of the load/save page. * Add prerequisites link to main pipeline. * Add better but not finished sidebar. * Add a couple new documentation pages. * Fix URLs in svg. * Incremental checkin. * Fix Youtube URLs. * Incremental checkin. * Minor fixes. * Add first pass at evaluation/deployment pages. * Minor spacing and link fixes. * Flesh out a number of additional pages and write basic compilation documentation. * Fix some minor issues, and add Docker deployment page (not totally finished yet). * Add developer documentation landing page. * Hopefully getting close to the final set of changes here. * Remove this documentation for now. * Fix a few links, and the size of the sidebar. * Fix some additional links. * Fix a bunch more links. * Fix another link that now has a better place. * Refactor test-docs.sh to handle documentation that is a standalone program. * Fix file exclusions. * Fully qualify typename. * Update name of file. * Fix syntax error. * Remove files that are not meant to be compiled. * Also skip the quickstart. * Move quickstart entry to the top. * Remove gray coloring of binding documentation. * Update name of sidebar link. * Update to working link. * Fix Wikipedia anchor.
92 lines
4.3 KiB
Markdown
92 lines
4.3 KiB
Markdown
# Community
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mlpack is a community-led effort, and so the code is not possible without the
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community. Since mlpack is an open-source project, anyone is welcome to become
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a part of the community and contribute. There is no need to be a a machine
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learning expert to participate; often, there are many tasks to be done that
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don't require in-depth knowledge.
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Over the past several years, mlpack has participated in
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[Google Summer of Code](https://summerofcode.withgoogle.com/). For more
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information, see [this page](gsoc.md).
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All mlpack development is done on [GitHub](https://github.com/mlpack/mlpack).
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Commits and issue comments can be tracked via the
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[mlpack-git](https://www.freelists.org/list/mlpack-git) list (graciously hosted
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by [FreeLists](https://www.freelists.org). Communication is generally either
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via issues on GitHub, or via chat:
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## Real-time chat
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* `#mlpack:matrix.org` on [Matrix](https://www.matrix.org/)
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* [mlpack Slack workspace](https://mlpack.slack.com/)
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- You will need to request an invite from the
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[auto-inviter](http://slack-inviter.mlpack.org:4000).
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- The Slack workspace is a bridged version of the Matrix room and it is
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generally a better experience to use Matrix directly.
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## Video meetup
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On the first Monday of every month, at ***1530 UTC***, we have *casual video
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meetups* with no particular agenda. Feel free to join up! We often talk about
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code changes that we are working on, issues that people are having with mlpack,
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general design direction, and whatever else might be on our mind.
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We use [this Zoom room](https://zoom.us/j/3820896170). For security, we use a
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password for the meeting to keep malicious bots out. The password is simple:
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it's just the name of the library (in all lowercase).
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## Getting involved
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Everyone is welcome to contribute to mlpack. But before becoming a contributor,
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it's often useful to understand mlpack as a user. So, a good place to start is
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to:
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- [download mlpack](https://www.mlpack.org/download.html)
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- use it in C++ (see [the documentation](../index.md))
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- use the bindings to other languages to perform machine learning tasks:
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[Python](../user/bindings/python.md), [Julia](../user/bindings/julia.md),
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[R](../user/bindings/r.md), [Go](../user/bindings/go.md), and the
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[command-line programs](../user/bindings/cli.md)
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There is also the [examples repository](https://github.com/mlpack/examples) that
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contains many examples you can build and play around with.
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Once you have an idea of what's included in mlpack and how a user might use it,
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then a good next step would be to set up a development environment. Once you
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have that set up, you can [build mlpack from source](../user/install.md), and
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explore the codebase to see how it's organized.
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Try making small changes to the code, or adding new tests to the test suite, and
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then rebuild to see how your changes work.
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Now you're set up to contribute! There are lots of ways you can contribute.
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Here are a couple ideas:
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* Help others figure out their mlpack issues and questions.
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[Here](https://github.com/mlpack/mlpack/issues?q=is%3Aopen+is%3Aissue+label%3A%22t%3A+question%22)
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is a list of GitHub issues tagged `question`. Helping others figure out
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their problems is really one of the best ways to learn about the library.
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* Read through the [vision document](https://www.mlpack.org/papers/vision.pdf)
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to learn about the development goals of the mlpack community and see the
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high-level tasks that need to be done to accomplish that vision.
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* Find an issue that needs implementation help;
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[here](https://github.com/mlpack/mlpack/issues) is the list of issues.
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* Find an abandoned pull request;
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[here](https://github.com/mlpack/mlpack/pulls?q=is%3Aclosed+is%3Apr+label%3A%22s%3A+stale%22)
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is a list of pull requests that were closed for inactivity. Often these have
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comments that need to be addressed, but the original author didn't have time
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to finish the work. So, you can pick up where they left off!
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* Implement a new machine learning algorithm that mlpack doesn't currently
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have.
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* Take a look at the ideas on the Google Summer of Code
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[Ideas List](https://github.com/mlpack/mlpack/wiki/SummerOfCOdeIdeas) and see
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if you find any of them interesting or exciting. Even if you're not planning
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to apply for Summer of Code, it's okay to take these ideas and implement them
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separately.
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