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.
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Tutorials and Examples
mlpack has a number of examples, video tutorials, and other resources showing usage of the library.
- mlpack examples repository: contains simple examples of mlpack usage for various machine learning tasks, in C++ and other languages. Both notebooks and standalone programs are available.
- mlpack Youtube channel: tutorial videos
for getting started with mlpack.
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Installing mlpack for use in C++: a step-by-step tutorial for installing and using mlpack from C++.
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Using mlpack for command-line data science: a demonstration of mlpack's command-line bindings.
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Simple data science workflow in C++ with mlpack: a tutorial using random forests and softmax regression in C++ to solve a simple data science problem.
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Development workflow tutorial: VSCode: set up an mlpack development environment in VSCode. This is useful if you are interested in contributing to mlpack.
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Development workflow tutorial: command-line: set up an mlpack development environment from the command-line. This is useful if you are interested in contributing to mlpack.
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- mlpack models repository: contains implementations of specific deep learning models that are too large or complex for inclusion in the main mlpack library.