---
visibility: public
---

# Machine Learning

**repo:** [josephmisiti/awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning)  
**category:** [[computer-science|Computer Science]]
**related:** [[python|Python]] · [[deep-learning|Deep Learning]] · [[data-science|Data Science]] · [[computer-vision|Computer Vision]] · [[generative-ai|Generative AI]]

---

# Awesome Machine Learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Track Awesome List](https://www.trackawesomelist.com/badge.svg)](https://www.trackawesomelist.com/josephmisiti/awesome-machine-learning/)

A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by `awesome-php`.

_If you want to contribute to this list (please do), send me a pull request or contact me [@josephmisiti](https://twitter.com/josephmisiti)._
Also, a listed repository should be deprecated if:

* Repository's owner explicitly says that "this library is not maintained".
* Not committed for a long time (2~3 years).

Further resources:

* For a list of free machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) books available for download, go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md).

* For a list of professional machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) events, go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/events.md).

* For a list of (mostly) free machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) courses available online, go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/courses.md).

* For a list of blogs and newsletters on [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning), go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/blogs.md).

* For a list of free-to-attend meetups and local events, go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/meetups.md).

## Star History

<a href="https://www.star-history.com/?repos=josephmisiti%2Fawesome-machine-learning&type=date&legend=top-left">
 <picture>
   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=josephmisiti/awesome-machine-learning&type=date&theme=dark&legend=top-left" />
   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=josephmisiti/awesome-machine-learning&type=date&legend=top-left" />
   <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=josephmisiti/awesome-machine-learning&type=date&legend=top-left" />
 </picture>
</a>

## Table of Contents

### Frameworks and Libraries

- [Awesome Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) ![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](#[awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome)-machine-learning-)
  - [Table of Contents](#table-of-contents)
    - [Frameworks and Libraries](#[frameworks](/@harrisonqian/awesome/wiki/front-end-development/frameworks)-and-libraries)
    - [Tools](#tools)
  - [APL](#apl)
      - [General-Purpose Machine Learning](#apl-general-purpose-machine-learning)
  - [C](#c)
      - [General-Purpose Machine Learning](#c-general-purpose-machine-learning)
      - [Computer Vision](#c-computer-vision)
  - [C++](#cpp)
      - [Computer Vision](#cpp-computer-vision)
      - [General-Purpose Machine Learning](#cpp-general-purpose-machine-learning)
      - [Natural Language Processing](#cpp-natural-language-processing)
      - [Speech Recognition](#cpp-speech-recognition)
      - [Sequence Analysis](#cpp-sequence-analysis)
      - [Gesture Detection](#cpp-gesture-detection)
      - [Reinforcement Learning](#cpp-reinforcement-learning)
  - [Common Lisp](#common-lisp)
      - [General-Purpose Machine Learning](#common-lisp-general-purpose-machine-learning)
  - [Clojure](#clojure)
      - [Natural Language Processing](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-natural-language-processing)
      - [General-Purpose Machine Learning](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-general-purpose-machine-learning)
      - [Deep Learning](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-deep-learning)
      - [Data Analysis](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-data-analysis--data-visualization)
      - [Data Visualization](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-data-visualization)
      - [Interop](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-interop)
      - [Misc](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-misc)
      - [Extra](#[clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-extra)
  - [Crystal](#crystal)
      - [General-Purpose Machine Learning](#[crystal](/@harrisonqian/awesome/wiki/programming-languages/crystal)-general-purpose-machine-learning)
  - [CUDA PTX](#cuda-ptx)
      - [Neurosymbolic AI](#cuda-ptx-neurosymbolic-ai)
  - [Elixir](#elixir)
      - [General-Purpose Machine Learning](#[elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir)-general-purpose-machine-learning)
      - [Natural Language Processing](#[elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir)-natural-language-processing)
  - [Erlang](#erlang)
      - [General-Purpose Machine Learning](#[erlang](/@harrisonqian/awesome/wiki/programming-languages/erlang)-general-purpose-machine-learning)
  - [Fortran](#fortran)
      - [General-Purpose Machine Learning](#[fortran](/@harrisonqian/awesome/wiki/programming-languages/fortran)-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#[fortran](/@harrisonqian/awesome/wiki/programming-languages/fortran)-data-analysis--data-visualization)
  - [Go](#go)
      - [Natural Language Processing](#go-natural-language-processing)
      - [General-Purpose Machine Learning](#go-general-purpose-machine-learning)
      - [Spatial analysis and geometry](#go-spatial-analysis-and-geometry)
      - [Data Analysis / Data Visualization](#go-data-analysis--data-visualization)
      - [Computer vision](#go-computer-vision)
      - [Reinforcement learning](#go-reinforcement-learning)
  - [Haskell](#haskell)
      - [General-Purpose Machine Learning](#[haskell](/@harrisonqian/awesome/wiki/programming-languages/haskell)-general-purpose-machine-learning)
  - [Java](#java)
      - [Natural Language Processing](#[java](/@harrisonqian/awesome/wiki/programming-languages/java)-natural-language-processing)
      - [General-Purpose Machine Learning](#[java](/@harrisonqian/awesome/wiki/programming-languages/java)-general-purpose-machine-learning)
      - [Speech Recognition](#[java](/@harrisonqian/awesome/wiki/programming-languages/java)-speech-recognition)
      - [Data Analysis / Data Visualization](#[java](/@harrisonqian/awesome/wiki/programming-languages/java)-data-analysis--data-visualization)
      - [Deep Learning](#[java](/@harrisonqian/awesome/wiki/programming-languages/java)-deep-learning)
  - [Javascript](#javascript)
      - [Natural Language Processing](#[javascript](/@harrisonqian/awesome/wiki/programming-languages/javascript)-natural-language-processing)
      - [Data Analysis / Data Visualization](#[javascript](/@harrisonqian/awesome/wiki/programming-languages/javascript)-data-analysis--data-visualization)
      - [General-Purpose Machine Learning](#[javascript](/@harrisonqian/awesome/wiki/programming-languages/javascript)-general-purpose-machine-learning)
      - [Misc](#[javascript](/@harrisonqian/awesome/wiki/programming-languages/javascript)-misc)
      - [Demos and Scripts](#[javascript](/@harrisonqian/awesome/wiki/programming-languages/javascript)-demos-and-scripts)
  - [Julia](#julia)
      - [General-Purpose Machine Learning](#[julia](/@harrisonqian/awesome/wiki/programming-languages/julia)-general-purpose-machine-learning)
      - [Natural Language Processing](#[julia](/@harrisonqian/awesome/wiki/programming-languages/julia)-natural-language-processing)
      - [Data Analysis / Data Visualization](#[julia](/@harrisonqian/awesome/wiki/programming-languages/julia)-data-analysis--data-visualization)
      - [Misc Stuff / Presentations](#[julia](/@harrisonqian/awesome/wiki/programming-languages/julia)-misc-stuff--presentations)
  - [Kotlin](#kotlin)
      - [Deep Learning](#[kotlin](/@harrisonqian/awesome/wiki/programming-languages/kotlin)-deep-learning)
  - [Lua](#lua)
      - [General-Purpose Machine Learning](#lua-general-purpose-machine-learning)
      - [Demos and Scripts](#lua-demos-and-scripts)
  - [Matlab](#matlab)
      - [Computer Vision](#matlab-computer-vision)
      - [Natural Language Processing](#matlab-natural-language-processing)
      - [General-Purpose Machine Learning](#matlab-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#matlab-data-analysis--data-visualization)
  - [.NET](#net)
      - [Computer Vision](#net-computer-vision)
      - [Natural Language Processing](#net-natural-language-processing)
      - [General-Purpose Machine Learning](#net-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#net-data-analysis--data-visualization)
  - [Objective C](#objective-c)
    - [General-Purpose Machine Learning](#objective-c-general-purpose-machine-learning)
  - [OCaml](#ocaml)
    - [General-Purpose Machine Learning](#[ocaml](/@harrisonqian/awesome/wiki/programming-languages/ocaml)-general-purpose-machine-learning)
  - [OpenCV](#opencv)
    - [Computer Vision](#opencv-Computer-Vision)
    - [Text-Detection](#Text-Character-Number-Detection)
  - [Perl](#perl)
    - [Data Analysis / Data Visualization](#[perl](/@harrisonqian/awesome/wiki/programming-languages/perl)-data-analysis--data-visualization)
    - [General-Purpose Machine Learning](#[perl](/@harrisonqian/awesome/wiki/programming-languages/perl)-general-purpose-machine-learning)
  - [Perl 6](#[perl](/@harrisonqian/awesome/wiki/programming-languages/perl)-6)
    - [Data Analysis / Data Visualization](#[perl](/@harrisonqian/awesome/wiki/programming-languages/perl)-6-data-analysis--data-visualization)
    - [General-Purpose Machine Learning](#[perl](/@harrisonqian/awesome/wiki/programming-languages/perl)-6-general-purpose-machine-learning)
  - [PHP](#php)
    - [Natural Language Processing](#php-natural-language-processing)
    - [General-Purpose Machine Learning](#php-general-purpose-machine-learning)
  - [Python](#python)
      - [Computer Vision](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-computer-vision)
      - [Natural Language Processing](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-natural-language-processing)
      - [General-Purpose Machine Learning](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-data-analysis--data-visualization)
      - [Misc Scripts / iPython Notebooks / Codebases](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-misc-scripts--ipython-notebooks--codebases)
      - [Neural Networks](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-neural-networks)
      - [Survival Analysis](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-survival-analysis)
      - [Federated Learning](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-federated-learning)
      - [Kaggle Competition Source Code](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-kaggle-competition-source-code)
      - [Reinforcement Learning](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-reinforcement-learning)
      - [Speech Recognition](#[python](/@harrisonqian/awesome/wiki/programming-languages/python)-speech-recognition)
  - [Ruby](#ruby)
      - [Natural Language Processing](#[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-natural-language-processing)
      - [General-Purpose Machine Learning](#[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-data-analysis--data-visualization)
      - [Misc](#[ruby](/@harrisonqian/awesome/wiki/programming-languages/ruby)-misc)
  - [Rust](#rust)
      - [General-Purpose Machine Learning](#[rust](/@harrisonqian/awesome/wiki/programming-languages/rust)-general-purpose-machine-learning)
      - [Deep Learning](#[rust](/@harrisonqian/awesome/wiki/programming-languages/rust)-deep-learning)
      - [Natural Language Processing](#[rust](/@harrisonqian/awesome/wiki/programming-languages/rust)-natural-language-processing)
  - [R](#r)
      - [General-Purpose Machine Learning](#r-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#r-data-analysis--data-visualization)
  - [SAS](#sas)
      - [General-Purpose Machine Learning](#sas-general-purpose-machine-learning)
      - [Data Analysis / Data Visualization](#sas-data-analysis--data-visualization)
      - [Natural Language Processing](#sas-natural-language-processing)
      - [Demos and Scripts](#sas-demos-and-scripts)
  - [Scala](#scala)
      - [Natural Language Processing](#[scala](/@harrisonqian/awesome/wiki/programming-languages/scala)-natural-language-processing)
      - [Data Analysis / Data Visualization](#[scala](/@harrisonqian/awesome/wiki/programming-languages/scala)-data-analysis--data-visualization)
      - [General-Purpose Machine Learning](#[scala](/@harrisonqian/awesome/wiki/programming-languages/scala)-general-purpose-machine-learning)
  - [Scheme](#scheme)
      - [Neural Networks](#scheme-neural-networks)
  - [Swift](#swift)
      - [General-Purpose Machine Learning](#[swift](/@harrisonqian/awesome/wiki/programming-languages/swift)-general-purpose-machine-learning)
  - [TensorFlow](#tensorflow)
      - [General-Purpose Machine Learning](#[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-general-purpose-machine-learning)

### [Tools](#tools-1)

- [Neural Networks](#tools-neural-networks)
- [Misc](#tools-misc)

[Credits](#credits)

<a name="apl"></a>
## APL

<a name="apl-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
* [naive-apl](https://github.com/mattcunningham/naive-apl) - Naive Bayesian Classifier implementation in APL. **[Deprecated]**

<a name="c"></a>
## C

<a name="c-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
* [Darknet](https://github.com/pjreddie/darknet) - Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
* [Recommender](https://github.com/GHamrouni/Recommender) - A C library for product recommendations/suggestions using collaborative filtering (CF).
* [Hybrid Recommender System](https://github.com/SeniorSA/hybrid-rs-trainner) - A hybrid recommender system based upon scikit-learn [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). **[Deprecated]**
* [neonrvm](https://github.com/siavashserver/neonrvm) - neonrvm is an open source machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library based on RVM technique. It's written in C programming language and comes with [Python](/@harrisonqian/awesome/wiki/programming-languages/python) programming language bindings.
* [cONNXr](https://github.com/alrevuelta/cONNXr) - An `ONNX` runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) models no matter which framework you train it with. Easy to install and compiles everywhere, even in very old devices.
* [libonnx](https://github.com/xboot/libonnx) - A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
* [onnx-c](https://github.com/onnx/onnx-c) - A lightweight C library for ONNX model inference, optimized for performance and portability across platforms.
* [qsmm](http://qsmm.org) - A C library implementing the rudiments of a toolchain for working with adaptive probabilistic assembler programs.

<a name="c-computer-vision"></a>
#### Computer Vision

* [CCV](https://github.com/liuliu/ccv) - C-based/Cached/Core [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) Library, A Modern [Computer Vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) Library.
* [VLFeat](http://www.vlfeat.org/) - VLFeat is an open and portable library of [computer vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms), which has a Matlab toolbox.
* [YOLOv8](https://github.com/ultralytics/ultralytics) - Ultralytics' YOLOv8 implementation with C++ support for real-time object detection and tracking, optimized for edge devices.
* [SpecX](https://specx.pro) - Specialized AI vision for extracting engineering specs from PDF/JPG to Excel.

<a name="cpp"></a>
## C++

<a name="cpp-computer-vision"></a>
#### Computer Vision

* [DLib](http://dlib.net/imaging.html) - DLib has C++ and [Python](/@harrisonqian/awesome/wiki/programming-languages/python) interfaces for face detection and training general object detectors.
* [EBLearn](http://eblearn.sourceforge.net/) - Eblearn is an object-oriented C++ library that implements various machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) models **[Deprecated]**
* [OpenCV](https://opencv.org) - OpenCV has C++, C, [Python](/@harrisonqian/awesome/wiki/programming-languages/python), [Java](/@harrisonqian/awesome/wiki/programming-languages/java) and MATLAB interfaces and supports [Windows](/@harrisonqian/awesome/wiki/platforms/windows), [Linux](/@harrisonqian/awesome/wiki/platforms/linux), [Android](/@harrisonqian/awesome/wiki/platforms/android) and Mac OS.
* [VIGRA](https://github.com/ukoethe/vigra) - VIGRA is a genertic [cross-platform](/@harrisonqian/awesome/wiki/platforms/cross-platform) C++ [computer vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) and machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library for volumes of arbitrary dimensionality with [Python](/@harrisonqian/awesome/wiki/programming-languages/python) bindings.
* [Openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) - A real-time multi-person keypoint detection library for body, face, hands, and foot estimation

<a name="cpp-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* * [Agentic Context Engine](https://github.com/kayba-ai/agentic-context-engine) -In-context [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) framework that allows agents to learn from execution feedback.
* [Speedster](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/speedster) -Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware. [DEEP LEARNING]
* [BanditLib](https://github.com/jkomiyama/banditlib) - A simple Multi-armed Bandit library. **[Deprecated]**
* [Caffe](https://github.com/BVLC/caffe) - A [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
* [CNTK](https://github.com/Microsoft/CNTK) - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning) toolkit that describes neural networks as a series of computational steps via a directed graph.
* [CUDA](https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
* [DeepDetect](https://github.com/jolibrain/deepdetect) - A machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
* [Distributed Machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Tool Kit (DMTK)](http://www.dmtk.io/) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: LightLDA and Distributed (Multisense) Word Embedding.
* [DLib](http://dlib.net/ml.html) - A suite of ML tools designed to be easy to imbed in other applications.
* [DSSTNE](https://github.com/amznlabs/amazon-dsstne) - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
* [DyNet](https://github.com/clab/dynet) - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [Fido](https://github.com/FidoProject/Fido) - A highly-modular C++ machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library for embedded [electronics](/@harrisonqian/awesome/wiki/hardware/electronics) and [robotics](/@harrisonqian/awesome/wiki/hardware/robotics).
* [FlexML](https://github.com/ozguraslank/flexml) - Easy-to-use and flexible AutoML library for [Python](/@harrisonqian/awesome/wiki/programming-languages/python).
* [igraph](http://igraph.org/) - General purpose graph library.
* [Intel® oneAPI Data [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) Library](https://github.com/oneapi-src/oneDAL) - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data [analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) and allows to process data in batch, online and distributed modes.
* [LightGBM](https://github.com/Microsoft/LightGBM) - Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms), used for ranking, classification and many other machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) tasks.
* [libfm](https://github.com/srendle/libfm) - A generic approach that allows to mimic most factorization models by feature engineering.
* [MCGrad](https://github.com/facebookincubator/MCGrad/) - A production-ready library for multicalibration, fairness, and bias correction in machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) models.
* [MLDB](https://mldb.ai) - The Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [Database](/@harrisonqian/awesome/wiki/databases/database) is a [database](/@harrisonqian/awesome/wiki/databases/database) designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
* [mlpack](https://www.mlpack.org/) - A scalable C++ machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library.
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with Dynamic, Mutation-aware Dataflow Dep Scheduler; for [Python](/@harrisonqian/awesome/wiki/programming-languages/python), R, [Julia](/@harrisonqian/awesome/wiki/programming-languages/julia), Go, [JavaScript](/@harrisonqian/awesome/wiki/programming-languages/javascript) and more.
* [N2D2](https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source [cross-platform](/@harrisonqian/awesome/wiki/platforms/cross-platform) performance library for [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) applications.
* [Opik](https://www.comet.com/site/products/opik/) - Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. ([Source Code](https://github.com/comet-ml/opik/))
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) that automates machine learning workflows.
* [PyCUDA](https://mathema.tician.de/software/pycuda/) - [Python](/@harrisonqian/awesome/wiki/programming-languages/python) interface to CUDA
* [ROOT](https://root.cern.ch) - A modular scientific software framework. It provides all the functionalities needed to deal with [big data](/@harrisonqian/awesome/wiki/big-data/big-data) processing, statistical analysis, visualization and storage.
* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html) - A fast, modular, feature-rich open-source C++ machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library.
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Toolbox.
* [sofia-ml](https://code.google.com/archive/p/sofia-ml) - Suite of fast incremental [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms).
* [Stan](http://mc-stan.org/) - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.
* [Timbl](https://languagemachines.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/timbl/) - A software package/C++ library implementing several memory-based [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms), among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a decision-tree approximation of IB1-IG. Commonly used for NLP.
* [Vowpal Wabbit (VW)](https://github.com/VowpalWabbit/vowpal_wabbit) - A fast out-of-[core](/@harrisonqian/awesome/wiki/platforms/core) [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) system.
* [Warp-CTC](https://github.com/baidu-research/warp-ctc) - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
* [XGBoost](https://github.com/dmlc/xgboost) - A parallelized optimized general purpose gradient boosting library.
* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - A fast library for GBDTs and Random Forests on GPUs.
* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM library on GPUs and CPUs.
* [LKYDeepNN](https://github.com/mosdeo/LKYDeepNN) - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
* [xLearn](https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online [advertising](/@harrisonqian/awesome/wiki/miscellaneous/advertising) and recommender systems.
* [Featuretools](https://github.com/featuretools/featuretools) - A library for automated feature engineering. It excels at transforming transactional and relational [datasets](/@harrisonqian/awesome/wiki/miscellaneous/datasets) into feature matrices for machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) using reusable feature engineering "primitives".
* [skynet](https://github.com/Tyill/skynet) - A library for [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) neural networks, has C-interface, net set in [JSON](/@harrisonqian/awesome/wiki/miscellaneous/json). Written in C++ with bindings in [Python](/@harrisonqian/awesome/wiki/programming-languages/python), C++ and C#.
* [Feast](https://github.com/gojek/feast) - A feature store for the management, discovery, and access of machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) features. Feast provides a consistent view of feature data for both model training and model serving.
* [Hopsworks](https://github.com/logicalclocks/hopsworks) - A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache Hive and a feature serving [database](/@harrisonqian/awesome/wiki/databases/database), based on [MySQL](/@harrisonqian/awesome/wiki/databases/mysql) Cluster, for online applications.
* [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform for reproducible and scalable machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) and [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning).
* [QuestDB](https://questdb.io/) - A relational column-oriented [database](/@harrisonqian/awesome/wiki/databases/database) designed for real-time [analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) on time series and event data.
* [Phoenix](https://phoenix.arize.com) - Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
* [XAD](https://github.com/auto-differentiation/XAD) - Comprehensive backpropagation tool for C++.
* [Truss](https://truss.baseten.co) - An open source framework for packaging and serving ML models.
* [nndeploy](https://github.com/nndeploy/nndeploy) - An Easy-to-Use and High-Performance AI deployment framework.

<a name="cpp-natural-language-processing"></a>
#### Natural Language Processing

* [BLLIP Parser](https://github.com/BLLIP/bllip-parser) - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser).
* [colibri-core](https://github.com/proycon/colibri-core) - C++ library, command line tools, and [Python](/@harrisonqian/awesome/wiki/programming-languages/python) binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
* [CRF++](https://taku910.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/crfpp/) - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks. **[Deprecated]**
* [CRFsuite](http://www.chokkan.org/software/crfsuite/) - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. **[Deprecated]**
* [frog](https://github.com/LanguageMachines/frog) - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
* [libfolia](https://github.com/LanguageMachines/libfolia) - C++ library for the [FoLiA format](https://proycon.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/folia/)
* [MeTA](https://github.com/meta-toolkit/meta) - [MeTA : ModErn Text Analysis](https://meta-toolkit.org/) is a C++ Data Sciences Toolkit that facilitates mining big text data.
* [MIT Information Extraction Toolkit](https://github.com/mit-nlp/MITIE) - C, C++, and [Python](/@harrisonqian/awesome/wiki/programming-languages/python) tools for named entity recognition and relation extraction
* [ucto](https://github.com/LanguageMachines/ucto) - [Unicode](/@harrisonqian/awesome/wiki/miscellaneous/unicode)-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.
* [SentencePiece](https://github.com/google/sentencepiece) - A C++ library for unsupervised text tokenization and detokenization, widely used in modern NLP models.

<a name="cpp-speech-recognition"></a>
#### Speech Recognition
* [Kaldi](https://github.com/kaldi-asr/kaldi) - Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.
* [Vosk](https://github.com/alphacep/vosk-api) - An offline speech recognition toolkit with C++ support, designed for low-resource devices and multiple languages.

<a name="cpp-sequence-analysis"></a>
#### Sequence Analysis
* [ToPS](https://github.com/ayoshiaki/tops) - This is an object-oriented framework that facilitates the [integration](/@harrisonqian/awesome/wiki/platforms/integration) of probabilistic models for sequences over a user defined alphabet. **[Deprecated]**

<a name="cpp-gesture-detection"></a>
#### Gesture Detection
* [grt](https://github.com/nickgillian/grt) - The Gesture Recognition Toolkit (GRT) is a [cross-platform](/@harrisonqian/awesome/wiki/platforms/cross-platform), open-source, C++ machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library designed for real-time gesture recognition.

<a name="cpp-reinforcement-learning"></a>
#### Reinforcement Learning
* [RLtools](https://github.com/rl-tools/rl-tools) - The fastest deep reinforcement [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library for continuous control, implemented header-only in pure, dependency-free C++ (Python bindings available as well).

<a name="common-lisp"></a>
## Common Lisp

<a name="common-lisp-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [mgl](https://github.com/melisgl/mgl/) - Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
* [mgl-gpr](https://github.com/melisgl/mgl-gpr/) - Evolutionary [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms). **[Deprecated]**
* [cl-libsvm](https://github.com/melisgl/cl-libsvm/) - Wrapper for the libsvm support vector machine library. **[Deprecated]**
* [cl-online-learning](https://github.com/masatoi/cl-online-learning) - Online [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) (Perceptron, AROW, SCW, Logistic Regression).
* [cl-random-forest](https://github.com/masatoi/cl-random-forest) - Implementation of Random Forest in [Common Lisp](/@harrisonqian/awesome/wiki/programming-languages/common-lisp).

<a name="clojure"></a>
## Clojure

<a name="clojure-natural-language-processing"></a>
#### Natural Language Processing

* [Clojure-openNLP](https://github.com/dakrone/clojure-opennlp) - Natural Language Processing in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) (opennlp).
* [Infections-clj](https://github.com/r0man/inflections-clj) - [Rails](/@harrisonqian/awesome/wiki/back-end-development/rails)-like inflection library for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) and [ClojureScript](/@harrisonqian/awesome/wiki/programming-languages/clojurescript).

<a name="clojure-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [scicloj.ml](https://github.com/scicloj/scicloj.ml) -  A idiomatic [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library based on tech.ml.dataset with a unique approach for immutable data processing pipelines.
* [clj-ml](https://github.com/joshuaeckroth/clj-ml/) - A machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) built on top of Weka and friends.
* [clj-boost](https://gitlab.com/alanmarazzi/clj-boost) - Wrapper for XGBoost
* [Touchstone](https://github.com/ptaoussanis/touchstone) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) A/B [testing](/@harrisonqian/awesome/wiki/testing/testing) library.
* [Clojush](https://github.com/lspector/Clojush) - The Push programming language and the PushGP genetic programming system implemented in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure).
* [lambda-ml](https://github.com/cloudkj/lambda-ml) - Simple, concise implementations of machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) techniques and utilities in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure).
* [Infer](https://github.com/aria42/infer) - Inference and machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure). **[Deprecated]**
* [Encog](https://github.com/jimpil/enclog) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) wrapper for Encog (v3) (Machine-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) framework that specializes in neural-nets). **[Deprecated]**
* [Fungp](https://github.com/vollmerm/fungp) - A genetic programming library for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure). **[Deprecated]**
* [Statistiker](https://github.com/clojurewerkz/statistiker) - Basic Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure). **[Deprecated]**
* [clortex](https://github.com/htm-community/clortex) - General Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library using Numenta’s Cortical [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Algorithm. **[Deprecated]**
* [comportex](https://github.com/htm-community/comportex) - Functionally composable Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library using Numenta’s Cortical Learning Algorithm. **[Deprecated]**

<a name="clojure-deep-learning"></a>
#### Deep Learning
* [MXNet](https://mxnet.apache.org/versions/1.7.0/api/clojure) - Bindings to Apache MXNet - part of the MXNet project
* [Deep Diamond](https://github.com/uncomplicate/deep-diamond) - A fast [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) Tensor & [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) library
* [jutsu.ai](https://github.com/hswick/jutsu.ai) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) wrapper for deeplearning4j with some added syntactic sugar.
* [cortex](https://github.com/originrose/cortex) - Neural networks, regression and feature [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure).
* [Flare](https://github.com/aria42/flare) - Dynamic Tensor Graph library in [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) (think PyTorch, DynNet, etc.)
* [dl4clj](https://github.com/yetanalytics/dl4clj) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) wrapper for Deeplearning4j.

<a name="clojure-data-analysis--data-visualization"></a>
#### Data Analysis
* [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) dataframe library and pipeline for data processing and machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning)
* [Tablecloth](https://github.com/scicloj/tablecloth) - A dataframe grammar wrapping tech.ml.dataset, inspired by several R libraries
* [Panthera](https://github.com/alanmarazzi/panthera) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) API wrapping [Python](/@harrisonqian/awesome/wiki/programming-languages/python)'s Pandas library
* [Incanter](http://incanter.org/) - Incanter is a [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)-based, R-like platform for statistical computing and graphics.
* [PigPen](https://github.com/Netflix/PigPen) - Map-Reduce for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure).
* [Geni](https://github.com/zero-one-group/geni) - a [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) dataframe library that runs on [Apache Spark](/@harrisonqian/awesome/wiki/big-data/apache-spark)

<a name="clojure-data-visualization"></a>
#### Data Visualization
* [Hanami](https://github.com/jsa-aerial/hanami) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)(Script) library and framework for creating interactive visualization applications based in Vega-Lite (VGL) and/or Vega (VG) specifications. Automatic framing and layouts along with a powerful templating system for abstracting visualization specs
* [Saite](https://github.com/jsa-aerial/saite) -  [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)(Script) client/server application for dynamic interactive explorations and the creation of live shareable documents capturing them using Vega/Vega-Lite, CodeMirror, [markdown](/@harrisonqian/awesome/wiki/miscellaneous/markdown), and [LaTeX](/@harrisonqian/awesome/wiki/miscellaneous/latex)
* [Oz](https://github.com/metasoarous/oz) - Data visualisation using Vega/Vega-Lite and Hiccup, and a live-reload platform for literate-programming
* [Envision](https://github.com/clojurewerkz/envision) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) Data Visualisation library, based on Statistiker and D3.
* [Pink Gorilla Notebook](https://github.com/pink-gorilla/gorilla-notebook) - A [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure)/[Clojurescript](/@harrisonqian/awesome/wiki/programming-languages/clojurescript) notebook application/-library based on Gorilla-REPL
* [clojupyter](https://github.com/clojupyter/clojupyter) -  A [Jupyter](/@harrisonqian/awesome/wiki/miscellaneous/jupyter) kernel for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) - run Clojure code in [Jupyter](/@harrisonqian/awesome/wiki/miscellaneous/jupyter) Lab, Notebook and Console.
* [notespace](https://github.com/scicloj/notespace) - Notebook experience in your [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) namespace
* [Delight](https://github.com/datamechanics/delight) - A listener that streams your spark events logs to delight, a free and improved spark UI

<a name="clojure-interop"></a>
#### Interop

* [Java Interop](https://clojure.org/reference/java_interop) - [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) has Native [Java](/@harrisonqian/awesome/wiki/programming-languages/java) Interop from which Java's ML ecosystem can be accessed
* [JavaScript Interop](https://clojurescript.org/reference/javascript-api) - [ClojureScript](/@harrisonqian/awesome/wiki/programming-languages/clojurescript) has Native [JavaScript](/@harrisonqian/awesome/wiki/programming-languages/javascript) Interop from which [JavaScript](/@harrisonqian/awesome/wiki/programming-languages/javascript)'s ML ecosystem can be accessed
* [Libpython-clj](https://github.com/clj-python/libpython-clj) - Interop with [Python](/@harrisonqian/awesome/wiki/programming-languages/python)
* [ClojisR](https://github.com/scicloj/clojisr) - Interop with R and Renjin (R on the JVM)

<a name="clojure-misc"></a>
#### Misc
* [Neanderthal](https://neanderthal.uncomplicate.org/) - Fast [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) Matrix Library (native CPU, GPU, OpenCL, CUDA)
* [kixistats](https://github.com/MastodonC/kixi.stats) - A library of statistical distribution sampling and transducing functions
* [fastmath](https://github.com/generateme/fastmath) - A collection of functions for mathematical and statistical computing, macine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning), etc., wrapping several JVM libraries
* [matlib](https://github.com/atisharma/matlib) - A [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure) library of optimisation and control theory tools and convenience functions based on Neanderthal.

<a name="clojure-extra"></a>
#### Extra
* [Scicloj](https://scicloj.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/pages/libraries/) - Curated list of ML related resources for [Clojure](/@harrisonqian/awesome/wiki/programming-languages/clojure).

<a name="crystal"></a>
## Crystal

<a name="crystal-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [machine](https://github.com/mathieulaporte/machine) - Simple machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) algorithm.
* [crystal-fann](https://github.com/NeuraLegion/crystal-fann) - FANN (Fast Artificial Neural Network) binding.

<a name="cuda-ptx"></a>
## CUDA PTX

<a name="cuda-ptx-neurosymbolic-ai"></a>
#### Neurosymbolic AI

* [Knowledge3D (K3D)](https://github.com/danielcamposramos/Knowledge3D) - Sovereign GPU-native spatial AI architecture with PTX-first cognitive engine (RPN/TRM reasoning), tri-modal fusion (text/visual/audio), and 3D persistent memory ("Houses"). Features sub-100µs inference, procedural knowledge compression (69:1 ratio), and multi-agent swarm architecture. Zero external dependencies for [core](/@harrisonqian/awesome/wiki/platforms/core) inference paths.

<a name="elixir"></a>
## Elixir

<a name="elixir-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [Simple Bayes](https://github.com/fredwu/simple_bayes) - A Simple Bayes / Naive Bayes implementation in [Elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir).
* [emel](https://github.com/mrdimosthenis/emel) - A simple and functional machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library written in [Elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir).
* [Tensorflex](https://github.com/anshuman23/tensorflex) - [Tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) bindings for the [Elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir) programming language.

<a name="elixir-natural-language-processing"></a>
#### Natural Language Processing

* [Stemmer](https://github.com/fredwu/stemmer) - An English (Porter2) stemming implementation in [Elixir](/@harrisonqian/awesome/wiki/programming-languages/elixir).

<a name="erlang"></a>
## Erlang

<a name="erlang-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [Disco](https://github.com/discoproject/disco/) - Map Reduce in [Erlang](/@harrisonqian/awesome/wiki/programming-languages/erlang). **[Deprecated]**

<a name="fortran"></a>
## Fortran

<a name="fortran-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [neural-fortran](https://github.com/modern-fortran/neural-fortran) - A parallel neural net microframework.
Read the paper [here](https://arxiv.org/abs/1902.06714).

<a name="fortran-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization

* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose [Fortran](/@harrisonqian/awesome/wiki/programming-languages/fortran) library for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).

<a name="go"></a>
## Go

<a name="go-natural-language-processing"></a>
#### Natural Language Processing

* [Cybertron](https://github.com/nlpodyssey/cybertron) - Cybertron: the home planet of the Transformers in Go.
* [snowball](https://github.com/tebeka/snowball) - Snowball Stemmer for Go.
* [word-embedding](https://github.com/ynqa/word-embedding) - Word Embeddings: the full implementation of word2vec, GloVe in Go.
* [sentences](https://github.com/neurosnap/sentences) - Golang implementation of Punkt sentence tokenizer.
* [go-ngram](https://github.com/Lazin/go-ngram) - In-memory n-gram index with compression. *[Deprecated]*
* [paicehusk](https://github.com/Rookii/paicehusk) - Golang implementation of the Paice/Husk Stemming Algorithm. *[Deprecated]*
* [go-porterstemmer](https://github.com/reiver/go-porterstemmer) - A native Go clean room implementation of the Porter Stemming algorithm. **[Deprecated]**

<a name="go-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning

* [Spago](https://github.com/nlpodyssey/spago) - Self-contained Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) and Natural Language Processing library in Go.
* [birdland](https://github.com/rlouf/birdland) - A recommendation library in Go.
* [eaopt](https://github.com/MaxHalford/eaopt) - An evolutionary optimization library.
* [leaves](https://github.com/dmitryikh/leaves) - A pure Go implementation of the prediction part of GBRTs, including XGBoost and LightGBM.
* [gobrain](https://github.com/goml/gobrain) - Neural Networks written in Go.
* [go-featureprocessing](https://github.com/nikolaydubina/go-featureprocessing) - Fast and convenient feature processing for low latency machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) in Go.
* [go-mxnet-predictor](https://github.com/songtianyi/go-mxnet-predictor) - Go binding for MXNet c_predict_api to do inference with a pre-trained model.
* [go-ml-benchmarks](https://github.com/nikolaydubina/go-ml-benchmarks) — benchmarks of machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) inference for Go.
* [go-ml-transpiler](https://github.com/znly/go-ml-transpiler) - An open source Go transpiler for machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) models.
* [golearn](https://github.com/sjwhitworth/golearn) - Machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Go.
* [goml](https://github.com/cdipaolo/goml) - Machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library written in pure Go.
* [gorgonia](https://github.com/gorgonia/gorgonia) - [Deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) in Go.
* [goro](https://github.com/aunum/goro) - A high-level machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) library in the vein of Keras.
* [gorse](https://github.com/zhenghaoz/gorse) - An offline recommender system backend based on collaborative filtering written in Go.
* [therfoo](https://github.com/therfoo/therfoo) - An embedded [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) library for Go.
* [neat](https://github.com/jinyeom/neat) - Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). **[Deprecated]**
* [go-pr](https://github.com/daviddengcn/go-pr) - Pattern recognition package in Go lang. **[Deprecated]**
* [go-ml](https://github.com/alonsovidales/go_ml) - Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution. **[Deprecated]**
* [GoNN](https://github.com/fxsjy/gonn) - GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN. **[Deprecated]**
* [bayesian](https://github.com/jbrukh/bayesian) - Naive Bayesian Classification for Golang. **[Deprecated]**
* [go-galib](https://github.com/thoj/go-galib) - Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) library written in Go / Golang. **[Deprecated]**
* [Cloudforest](https://github.com/ryanbressler/CloudForest) - Ensembles of decision trees in Go/Golang. **[Deprecated]**
* [go-dnn](https://github.com/sudachen/go-dnn) - Deep Neural Networks for Golang (powered by MXNet)

<a name="go-spatial-analysis-and-geometry"></a>
#### Spatial analysis and geometry

* [go-geom](https://github.com/twpayne/go-geom) - Go library to handle geometries.
* [gogeo](https://github.com/golang/geo) - Spherical geometry in Go.

<a name="go-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization

* [dataframe-go](https://github.com/rocketlaunchr/dataframe-go) - Dataframes for machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning) and statistics (similar to pandas).
* [gota](https://github.com/go-gota/gota) - Dataframes.
* [gonum/mat](https://godoc.org/gonum.org/v1/gonum/mat) - A linear algebra package for Go.

---
*truncated — [full list on GitHub](https://github.com/josephmisiti/awesome-machine-learning)*
