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.
42 lines
2.1 KiB
Markdown
42 lines
2.1 KiB
Markdown
# Tutorials and Examples
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mlpack has a number of examples, video tutorials, and other resources showing
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usage of the library.
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* [mlpack examples repository](https://github.com/mlpack/examples/): contains
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simple examples of mlpack usage for various machine learning tasks, in C++
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and other languages. Both notebooks and standalone programs are available.
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---
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* [mlpack Youtube channel](https://www.youtube.com/@mlpack): tutorial videos
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for getting started with mlpack.
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- [Installing mlpack for use in C++](https://www.youtube.com/watch?v=wcEFce7IaS8):
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a step-by-step tutorial for installing and using mlpack from C++.
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* [Ubuntu/Debian](https://www.youtube.com/watch?v=wcEFce7IaS8&t=46s)
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* [Fedora/RHEL](https://www.youtube.com/watch?v=wcEFce7IaS8&t=188s)
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* [MacOS (via Homebrew)](https://www.youtube.com/watch?v=wcEFce7IaS8&t=303s)
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* [Installing from source](https://www.youtube.com/watch?v=wcEFce7IaS8&t=440s)
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* [Installing from source with the autodownloader](https://www.youtube.com/watch?v=wcEFce7IaS8&t=712s)
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- [Using mlpack for command-line data science](https://www.youtube.com/watch?v=M0DLrUVSyrE):
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a demonstration of mlpack's command-line bindings.
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- [Simple data science workflow in C++ with mlpack](https://www.youtube.com/watch?v=PD9AqGdkPl8):
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a tutorial using random forests and softmax regression in C++ to solve a
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simple data science problem.
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- [Development workflow tutorial: VSCode](https://www.youtube.com/watch?v=7DOrMQ2HhBY):
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set up an mlpack development environment in VSCode. *This is useful if you
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are interested in contributing to mlpack.*
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- [Development workflow tutorial: command-line](https://www.youtube.com/watch?v=3PgFzA5duwc):
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set up an mlpack development environment from the command-line. *This is
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useful if you are interested in contributing to mlpack.*
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---
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* [mlpack models repository](https://github.com/mlpack/models/): contains
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implementations of specific deep learning models that are too large or
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complex for inclusion in the main mlpack library.
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