mlpack
3.0.1

Quickstart Tutorials
These tutorials give very quick "getting started" examples that you can use to get started with mlpack in different languages.
Introductory Tutorials
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.
 Building mlpack From Source
 File formats and loading data in mlpack
 Matrices in mlpack
 Writing an mlpack binding
 mlpack Timers
 Simple Sample mlpack Programs
Methodspecific Tutorials
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.
 NeighborSearch tutorial (knearestneighbors)
 Linear/ridge regression tutorial (mlpack_linear_regression)
 RangeSearch tutorial (mlpack_range_search)
 Density Estimation Tree (DET) tutorial
 KMeans tutorial (kmeans)
 Fast maxkernel search tutorial (fastmks)
 EMST Tutorial
 Alternating Matrix Factorization tutorial
 Collaborative filtering tutorial
 Approximate furthest neighbor search (mlpack_approx_kfn) tutorial
 Neural Network tutorial
Advanced Tutorials
These tutorials discuss some of the more advanced functionality contained in mlpack.
 Optimizer implementation tutorial
 CNE Optimizer tutorial
 mlpack automatic bindings to other languages
 CrossValidation
 HyperParameter Tuning
Policy Class Documentation
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.
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