\section{Quickstart Tutorials}\label{tutorials_quickstart_tut}
These tutorials give very quick \char`\"{}getting started\char`\"{} examples that you can use to get started with mlpack in different languages.


\begin{DoxyItemize}
\item \doxyref{mlpack in Python quickstart guide}{p.}{python_quickstart}
\item \doxyref{mlpack command-\/line quickstart guide}{p.}{cli_quickstart}
\item \doxyref{mlpack in Julia quickstart guide}{p.}{julia_quickstart}
\item \doxyref{mlpack in Go quickstart guide}{p.}{go_quickstart}
\end{DoxyItemize}\section{Introductory Tutorials}\label{tutorials_introd_tut}
These tutorials introduce the basic concepts of working with mlpack, aimed at developers who want to use and contribute to mlpack but are not sure where to start.


\begin{DoxyItemize}
\item \doxyref{Building mlpack From Source}{p.}{build}
\item \doxyref{Building mlpack From Source on Windows}{p.}{build_windows}
\item \doxyref{File formats and loading data in mlpack}{p.}{formatdoc}
\item \doxyref{Matrices in mlpack}{p.}{matrices}
\item \doxyref{Writing an mlpack binding}{p.}{iodoc}
\item \doxyref{mlpack Timers}{p.}{timer}
\item \doxyref{Simple Sample mlpack Programs}{p.}{sample}
\item \doxyref{Sample C++ ML App for Windows}{p.}{sample_ml_app}
\end{DoxyItemize}\section{Method-\/specific Tutorials}\label{tutorials_method_tut}
These tutorials introduce the various methods mlpack offers, aimed at users who want to get started quickly. These tutorials start with simple examples and progress to complex, extensible uses.


\begin{DoxyItemize}
\item \doxyref{Neighbor\+Search tutorial (k-\/nearest-\/neighbors)}{p.}{nstutorial}
\item \doxyref{Linear/ridge regression tutorial (mlpack\+\_\+linear\+\_\+regression)}{p.}{lrtutorial}
\item \doxyref{Range\+Search tutorial (mlpack\+\_\+range\+\_\+search)}{p.}{rstutorial}
\item \doxyref{Density Estimation Tree (D\+ET) tutorial}{p.}{dettutorial}
\item \doxyref{K-\/\+Means tutorial (kmeans)}{p.}{kmtutorial}
\item \doxyref{Fast max-\/kernel search tutorial (fastmks)}{p.}{fmkstutorial}
\item \doxyref{E\+M\+ST Tutorial}{p.}{emst_tutorial}
\item \doxyref{Alternating Matrix Factorization tutorial}{p.}{amftutorial}
\item \doxyref{Collaborative filtering tutorial}{p.}{cftutorial}
\item \doxyref{Approximate furthest neighbor search (mlpack\+\_\+approx\+\_\+kfn) tutorial}{p.}{akfntutorial}
\item \doxyref{Neural Network tutorial}{p.}{anntutorial}
\end{DoxyItemize}\section{Advanced Tutorials}\label{tutorials_adv_tut}
These tutorials discuss some of the more advanced functionality contained in mlpack.


\begin{DoxyItemize}
\item \doxyref{mlpack automatic bindings to other languages}{p.}{bindings}
\item \doxyref{Cross-\/\+Validation}{p.}{cv}
\item \doxyref{Hyper-\/\+Parameter Tuning}{p.}{hpt_guide}
\end{DoxyItemize}\section{Policy Class Documentation}\label{tutorials_policy_tut}
mlpack uses templates to achieve its genericity and flexibility. Some of the template types used by mlpack are common across multiple machine learning algorithms. The links below provide documentation for some of these common types.


\begin{DoxyItemize}
\item \doxyref{The Metric\+Type policy in mlpack}{p.}{metrics}
\item \doxyref{The Kernel\+Type policy in mlpack}{p.}{kernels}
\item \doxyref{The Tree\+Type policy in mlpack}{p.}{trees} 
\end{DoxyItemize}