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