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
- File formats in mlpack
- Matrices in mlpack
- mlpack Input and Output
- mlpack Timers
- Simple Sample mlpack Programs
- Hyper-Parameter Tuning
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
- CNE Optimizer tutorial
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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