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1980:, the purpose of this library is to facilitate the transition between CPU and GPU by making a minor changes to the source code, (e.g. changing the namespace, and the linking library). mlpack currently supports partially Bandicoot with objective to provide neural network training on the GPU. The following examples shows two code blocks executing an identical operation. The first one is Armadillo code and it is running on the CPU, while the second one can runs on OpenCL supported GPU or NVIDIA GPU (with CUDA backend)
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244:, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers.
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mlpack is low dependencies library which makes it perfect for easy deployment of software. mlpack binaries can be linked statically and deployed to any system with minimal effort. The usage of Docker container is not necessary and even discouraged. This makes it suitable for low resource devices, as
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mlpack includes a range of design features that make it particularly well-suited for specialized applications, especially in the Edge AI and IoT domains. Its C++ codebase allows for seamless integration with sensors, facilitating direct data extraction and on-device preprocessing at the Edge. Below,
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Armadillo is the default linear algebra library that is used by mlpack, it provide matrix manipulation and operation necessary for machine learning algorithms. Armadillo is know for its efficiency and simplicity. it can also be used in header-only mode, and the only library we need to link against
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In terms of binary size, mlpack methods have a significantly smaller footprint compared to other popular libraries. Below, we present a comparison of deployable binary sizes between mlpack, PyTorch, and scikit-learn. To ensure consistency, the same application, along with all its dependencies, was
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ensmallen is a high quality C++ library for non linear numerical optimizer, it uses
Armadillo or bandicoot for linear algebra and it is used by mlpack to provide optimizer for training machine learning algorithms. Similar to mlpack, ensmallen is a header-only library and supports custom behavior
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ensmallen contains a diverse range of optimizer classified based on the function type (differentiable, partially differentiable, categorical, constrained, etc). In the following we list a small set of optimizer that available in ensmallen. For the full list please check this documentation
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mlpack contains a wide range of algorithms that are used to solved real problems from classification and regression in the
Supervised learning paradigm to clustering and dimension reduction algorithms. In the following, a non exhaustive list of
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mlpack contains several
Reinforcement Learning (RL) algorithms implemented in C++ with a set of examples as well, these algorithms can be tuned per examples and combined with external simulators. Currently mlpack supports the following:
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The following shows a simple example how to train a decision tree model using mlpack, and to use it for the classification. Of course you can ingest your own dataset using the Load function, but for now we are showing the
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numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent
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1933:). Our objective is to simplify for the user the API and the main machine learning functions such as Classify and Predict. More complex examples are located in the
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Bandicoot is a C++ Linear
Algebra library designed for scientific computing, it has the an identical API to Armadillo with objective to execute the computation on
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using callbacks functions allowing the users to extend the functionalities for any optimizer. In addition ensmallen is published under the
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it requires only the ensmallen and
Armadillo or Bandicoot depending on the type of hardware we are planning to deploy to. mlpack uses
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Tree-based
Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either kd-trees or cover trees
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library for serialization of the models. Other dependencies are also header-only and part of the library itself.
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we outline a specific set of design features that highlight mlpack's capabilities in these environments:
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The above example demonstrate the simplicity behind the API design, which makes it similar to popular
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1563:// Train a decision tree on random numeric data and predict labels on test data:
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2486:"Initial checkin of the regression package to be released · mlpack/mlpack"
1566:// All data and labels are uniform random; 10 dimensional data, 5 classes.
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1569:// Replace with a data::Load() call or similar for a real application.
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packaged within a single Docker container for this comparison.
2572:"mlpack 4: a fast, header-only C++ machine learning library"
2531:"The ensmallen library for flexible numerical optimization"
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structures are available, thus the library also supports
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Limited memory
Broyden–Fletcher–Goldfarb–Shanno (L-BFGS)
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List of datasets in computer vision and image processing
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1857:// Print some information about the test predictions.
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2297:Covariance matrix adaptation evolution strategy
2618:"C++ library for GPU accelerated linear algebra"
2385:Stochastic Gradient Descent with Restarts (SGDR)
2412:mlpack is fiscally sponsored and supported by
1152:List of datasets for machine-learning research
1899:" test points classified as class "
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8:
16:
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259:license. The project is supported by the
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263:and contributions from around the world.
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2420:program and mentors several students.
1205:Classical machine learning algorithms
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2535:Journal of Machine Learning Research
2438:Free and open-source software portal
2451:List of numerical analysis software
1267:Kernel Principal Component Analysis
1147:Glossary of artificial intelligence
1433:Deep Deterministic Policy Gradient
14:
2570:Ryan Curtin; et al. (2023).
2529:Ryan Curtin; et al. (2021).
1328:Non-negative Matrix Factorization
1322:Neighbourhood Components Analysis
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1244:Euclidean minimum spanning trees
2727:Free software programmed in C++
2576:Journal of Open Source Software
2381:Stochastic Gradient Descent SGD
261:Georgia Institute of Technology
1978:Graphics Processing Unit (GPU)
1545:Other libraries exist such as
1340:Independent component analysis
567:Relevance vector machine (RVM)
1:
1334:Principal Components Analysis
1056:Computational learning theory
620:Expectation–maximization (EM)
1929:based machine learning kit (
1494:Forest covertype classifier
1013:Coefficient of determination
860:Convolutional neural network
572:Support vector machine (SVM)
59:4.5.0 / 18 September 2024
2456:List of numerical libraries
1854:// Step 3: classify points.
1164:Outline of machine learning
1061:Empirical risk minimization
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1453:Low number of dependencies
1368:Sparse dictionary learning
1297:Locality-Sensitive Hashing
1289:Bayesian Linear Regression
1234:(one-level decision trees)
801:Feedforward neural network
552:Artificial neural networks
2732:Free statistical software
2717:Free mathematics software
1389:Recurrent Neural Networks
1318:with dual-tree algorithms
1261:Kernel density estimation
784:Artificial neural network
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21:
2461:Numerical linear algebra
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1982:
1770:// Step 1: create model.
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1093:Journals and conferences
1040:Mathematical foundations
950:Temporal difference (TD)
806:Recurrent neural network
726:Conditional random field
649:Dimensionality reduction
397:Dimensionality reduction
359:Quantum machine learning
354:Neuromorphic engineering
314:Self-supervised learning
309:Semi-supervised learning
2712:Free computer libraries
2446:Armadillo (C++ library)
1803:// Step 2: train model.
1482:MNIST digit recognizer
1475:Binary size comparison
1439:Twin Delayed DDPG (TD3)
1375:Tree-based Range Search
1316:Nearest neighbor search
1293:Local Coordinate Coding
1249:Gaussian Mixture Models
1227:Collaborative Filtering
502:Apprenticeship learning
226:artificial intelligence
1419:Reinforcement learning
1399:There are bindings to
1311:Naive Bayes Classifier
1278:Least-Angle Regression
1051:Bias–variance tradeoff
933:Reinforcement learning
909:Spiking neural network
319:Reinforcement learning
251:distributed under the
232:, built on top of the
36:; 16 years ago
2722:Free science software
2418:Google Summer of Code
1490:(Softmax regression)
1352:Simple Least-Squares
887:Neural radiance field
709:Structured prediction
432:Structured prediction
304:Unsupervised learning
220:software library for
34:February 1, 2008
2466:Scientific computing
1466:Low binary footprint
1379:Class templates for
1255:Hidden Markov Models
1076:Statistical learning
974:Learning with humans
766:Local outlier factor
249:open-source software
61:; 4 days ago
2622:coot.sourceforge.io
2557:2021arXiv210812981C
2510:. 18 September 2024
2362:NesterovMomentumSGD
1935:examples repository
1752:// 500 test points.
1488:Language detection
1476:
1303:Logistic regression
919:Electrochemical RAM
826:reservoir computing
557:Logistic regression
476:Supervised learning
462:Multimodal learning
437:Feature engineering
382:Generative modeling
344:Rule-based learning
339:Curriculum learning
299:Supervised learning
274:Part of a series on
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2604:"Mlpack/Mlpack.jl"
2488:. February 8, 2008
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1273:K-Means Clustering
1238:Density Estimation
487: •
402:Density estimation
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1436:Soft Actor-Critic
1354:Linear Regression
1345:Rank-Approximate
1307:Max-Kernel Search
1284:Linear Regression
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1007:Model diagnostics
990:Human-in-the-loop
833:Boltzmann machine
746:Anomaly detection
542:Linear regression
457:Ontology learning
452:Grammar induction
427:Semantic analysis
422:Association rules
407:Anomaly detection
349:Neuro-symbolic AI
234:Armadillo library
214:free, open-source
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66:18 September 2024
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1496:(decision tree)
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1358:Ridge Regression
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1141:Related articles
1018:Confusion matrix
771:Isolation forest
716:Graphical models
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447:Learning to rank
442:Feature learning
280:Machine learning
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2508:"Release 4.5.0"
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2377:SARAH/SARAH+
2353:Momentum SGD
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1957:are either
1884:predictions
1848:predictions
1842:testDataset
1824:predictions
1716:testDataset
1689:distr_param
955:Multi-agent
892:Transformer
791:Autoencoder
547:Naive Bayes
285:data mining
253:BSD license
228:written in
218:header-only
175:Open source
2696:Categories
2651:2024-08-12
2627:2024-08-12
2589:2302.00820
2548:2108.12981
2472:References
2292:FrankWolfe
1429:Q-learning
1222:supports:
1212:algorithms
940:Q-learning
838:Restricted
636:Mean shift
585:Clustering
562:Perceptron
490:regression
392:Clustering
387:Regression
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80:Repository
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2350:Lookahead
2306:AdaBelief
2262:ensmallen
2127:namespace
1988:namespace
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1951:Armadillo
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1081:VC theory
1028:ROC curve
960:Self-play
880:DeepDream
721:Bayes net
512:Ensembles
293:Paradigms
238:ensmallen
2424:See also
2414:NumFOCUS
2347:Katyusha
2329:AMSBound
2312:AdaDelta
2309:AdaBound
1963:IntelMKL
1959:OpenBLAS
1908:<<
1902:<<
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1836:Classify
1671:>>
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1536:1.03 MB
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2374:RMSProp
2359:NadaMax
2332:AMSGrad
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1530:mlpack
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93:/mlpack
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2018:randu
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