mlpack is a general-purpose machine learning library, written in C++, that aims to provide fast, lightweight implementations of both common and cutting-edge machine learning algorithms. It depends only on the Armadillo linear algebra library and the cereal serialization library.
mlpack is intended for academic and commercial use, for instance by data scientists who need efficiency and ease of deployment, or, e.g., by researchers who need flexibility and extensibility.
High-quality documentation is a development goal of mlpack. mlpack’s documentation is split into two parts: documentation for the bindings/CLI, and documentation for the C++ library. Also useful is the examples repository, which demonstrates usage of mlpack’s functionality in simple example programs.
Generally, working with the bindings is a good choice for simple machine learning and data science tasks, and writing C++ is a good idea when complex or custom functionality is desired.
All interfaces are heavily documented, and if you find a documentation issue, please report it.
• Building mlpack From Source (Linux)
• Building mlpack From Source (Windows)
• mlpack command-line quickstart guide
• command-line documentation
• mlpack in Python quickstart guide
• Binding documentation
• mlpack in Julia quickstart guide
• Binding documentation
• mlpack in R quickstart guide
• Binding documentation
• mlpack in Go quickstart guide
• Binding documentation
For details on the C++ API, it's recommended to look at the documentation in the source code; every class and method is fully documented in comments. Below are some tutorials and additional resources that can be used.
• Sample C++ ML App for Windows
• File formats and loading data in mlpack
• Matrices in mlpack
• Cross-Validation Tutorial
• Hyperparameter Tuner Tutorial
• Alternating Matrix Factorization (AMF)
• Artificial Neural Networks (ANN)
• Approximate k-furthest Neighbor Search (approx_kfn)
• Collaborative Filtering (CF)
• DatasetMapper
• Density Estimation Trees (DET)
• Euclidean Minimum Spanning Trees (EMST)
• Fast Max-Kernel Search (FastMKS)
• Image Utilities
• k-Means Clustering
• Linear Regression
• Neighbor Search (k-Nearest-Neighbors)
• Range Search
• Reinforcement Learning
• mlpack Timers
• mlpack versions in code
• The ElemType Policy in mlpack
• The MetricType Policy in mlpack
• The KernelType Policy in mlpack
• The TreeType Policy in mlpack
• mlpack Automatic Bindings To Other Languages
• Writing an mlpack Binding
© 2007 - 2023 mlpack developers (BSD License).
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