These tutorials give very quick "getting started" examples that you can use to get started with mlpack in different languages.
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
- Building mlpack From Source on Windows
- File formats and loading data in mlpack
- Matrices in mlpack
- Writing an mlpack binding
- mlpack Timers
- Simple Sample mlpack Programs
- Sample C++ ML App for Windows
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 (k-nearest-neighbors)
- Linear/ridge regression tutorial (mlpack_linear_regression)
- RangeSearch tutorial (mlpack_range_search)
- Density Estimation Tree (DET) tutorial
- K-Means tutorial (kmeans)
- Fast max-kernel search tutorial (fastmks)
- EMST Tutorial
- Alternating Matrix Factorization tutorial
- Collaborative filtering tutorial
- Approximate furthest neighbor search (mlpack_approx_kfn) tutorial
- Neural Network tutorial
These tutorials discuss some of the more advanced functionality contained in mlpack.
- Optimizer implementation tutorial
- CNE Optimizer tutorial
- mlpack automatic bindings to other languages
- Hyper-Parameter Tuning
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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