The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.
Open source C# framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence. Development has now shifted to GitHub.
An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) for machine learning and optimization applications. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use of differentiation, for applications such as hyperparameter optimization.
Cross platform wrapper of OpenCV which can be compiled in Mono to be run on Windows, Linus, Mac OS X, iOS, and Android.
An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
Multi-platform genetic algorithm library for .NET Core and .NET Framework. The library has several implementations of GA operators, like: selection, crossover, mutation, reinsertion and termination.
Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.
Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and everyday use. Supports .Net 4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers. ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.
.NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#. https://mxnet.tech-quantum.com/
DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feedback. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.
numl is a machine learning library intended to ease the use of using standard modelling techniques for both prediction and clustering.
A wrapper for the OpenCV project to be used with .NET applications.
Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for rapid development.
A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.
Neural network library in F#.
Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
This book teaches you how to take machine learning models from your personal laptop to large distributed clusters. You’ll explore key concepts and patterns behind successful distributed machine learning systems, and learn technologies like TensorFlow, Kubernetes, Kubeflow, and Argo Workflows directly from a key maintainer and contributor, with real-world scenarios and hands-on projects.
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
Learn the essentials of machine learning by completing a carefully designed set of real-world projects.
This blog provides a curated list of introductory books to help aspiring ML professionals to grasp foundational machine learning concepts and techniques.
Discover open source deep learning code and pretrained models.
An opensource viewer for neural network, deep learning and machine learning models
Free, no-signup APIs for text, image, and audio generation with no API keys required. Offers OpenAI-compatible interfaces and React hooks for easy integration.
Train Machine Learning models on the fly to recognize your own images, sounds, & poses.
C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.
An `ONNX` runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it with. Easy to install and compiles everywhere, even in very old devices.
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.
A hybrid recommender system based upon scikit-learn algorithms. **[Deprecated]**
A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
A C library for product recommendations/suggestions using collaborative filtering (CF).
VLFeat is an open and portable library of computer vision algorithms, which has a Matlab toolbox.
A simple Multi-armed Bandit library. **[Deprecated]**
BLLIP Natural Language Parser (also known as the Charniak-Johnson parser).
A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
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.
The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks. **[Deprecated]**
CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. **[Deprecated]**
This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
A machine 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.
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 has C++ and Python interfaces for face detection and training general object detectors.
A suite of ML tools designed to be easy to imbed in other applications.
A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
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.
Eblearn is an object-oriented C++ library that implements various machine learning models **[Deprecated]**
A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
A highly-modular C++ machine learning library for embedded electronics and robotics.
Easy-to-use and flexible AutoML library for Python.
Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.
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, based on MySQL Cluster, for online applications.
General purpose graph library.
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 and allows to process data in batch, online and distributed modes.
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.
A generic approach that allows to mimic most factorization models by feature engineering.
C++ library for the FoLiA format
Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
MeTA : ModErn Text Analysis is a C++ Data Sciences Toolkit that facilitates mining big text data.
C, C++, and Python tools for named entity recognition and relation extraction
The Machine Learning Database is a 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.
A scalable C++ machine learning library.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
An open-source cross-platform performance library for deep learning applications.
OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
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)
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.
Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
A platform for reproducible and scalable machine learning and deep learning.
A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
An open-source, low-code machine learning library in Python that automates machine learning workflows.
Python interface to CUDA
A relational column-oriented database designed for real-time analytics on time series and event data.
The fastest deep reinforcement learning library for continuous control, implemented header-only in pure, dependency-free C++ (Python bindings available as well).
A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.
A fast, modular, feature-rich open-source C++ machine learning library.
The Shogun Machine Learning Toolbox.
A library for learning neural networks, has C-interface, net set in JSON. Written in C++ with bindings in Python, C++ and C#.
Suite of fast incremental algorithms.
Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware. [DEEP LEARNING]
A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.
A fast library for GBDTs and Random Forests on GPUs.
A fast SVM library on GPUs and CPUs.
A software package/C++ library implementing several memory-based learning 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.
This is an object-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet. **[Deprecated]**
An open source framework for packaging and serving ML models.
Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.
VIGRA is a genertic cross-platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings.
A fast out-of-core learning system.
A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
Comprehensive backpropagation tool for C++.
A parallelized optimized general purpose gradient boosting library.
A high performance, easy-to-use, and scalable machine 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 and recommender systems.
Wrapper for XGBoost
A machine learning library for Clojure built on top of Weka and friends.
Interop with R and Renjin (R on the JVM)
A Jupyter kernel for Clojure - run Clojure code in Jupyter Lab, Notebook and Console.
Natural Language Processing in Clojure (opennlp).
The Push programming language and the PushGP genetic programming system implemented in Clojure.
General Machine Learning library using Numenta’s Cortical Learning Algorithm. **[Deprecated]**
Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm. **[Deprecated]**
Neural networks, regression and feature learning in Clojure.
A fast Clojure Tensor & Deep Learning library
A listener that streams your spark events logs to delight, a free and improved spark UI
Clojure wrapper for Deeplearning4j.
Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets). **[Deprecated]**
Clojure Data Visualisation library, based on Statistiker and D3.
A collection of functions for mathematical and statistical computing, macine learning, etc., wrapping several JVM libraries
Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)
A genetic programming library for Clojure. **[Deprecated]**
a Clojure dataframe library that runs on Apache Spark
: 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
Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
Rails-like inflection library for Clojure and ClojureScript.
Inference and machine learning in Clojure. **[Deprecated]**
Clojure has Native Java Interop from which Java's ML ecosystem can be accessed
ClojureScript has Native JavaScript Interop from which JavaScript's ML ecosystem can be accessed
Clojure wrapper for deeplearning4j with some added syntactic sugar.
A library of statistical distribution sampling and transducing functions
Simple, concise implementations of machine learning techniques and utilities in Clojure.
Interop with Python
A Clojure library of optimisation and control theory tools and convenience functions based on Neanderthal.
Bindings to Apache MXNet - part of the MXNet project
Fast Clojure Matrix Library (native CPU, GPU, OpenCL, CUDA)
Notebook experience in your Clojure namespace
Data visualisation using Vega/Vega-Lite and Hiccup, and a live-reload platform for literate-programming
Clojure API wrapping Python's Pandas library
Map-Reduce for Clojure.
A Clojure/Clojurescript notebook application/-library based on Gorilla-REPL
Clojure(Script) client/server application for dynamic interactive explorations and the creation of live shareable documents capturing them using Vega/Vega-Lite, CodeMirror, markdown, and LaTeX
Curated list of ML related resources for Clojure.
A idiomatic Clojure machine learning library based on tech.ml.dataset with a unique approach for immutable data processing pipelines.
Basic Machine Learning algorithms in Clojure. **[Deprecated]**
A dataframe grammar wrapping tech.ml.dataset, inspired by several R libraries
Clojure dataframe library and pipeline for data processing and machine learning
Clojure A/B testing library.
Wrapper for the libsvm support vector machine library. **[Deprecated]**
Online learning algorithms (Perceptron, AROW, SCW, Logistic Regression).
Implementation of Random Forest in Common Lisp.
Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
Evolutionary algorithms. **[Deprecated]**
Naive Bayesian Classification for Golang. **[Deprecated]**
A recommendation library in Go.
Ensembles of decision trees in Go/Golang. **[Deprecated]**
Cybertron: the home planet of the Transformers in Go.
Dataframes for machine-learning and statistics (similar to pandas).
An evolutionary optimization library.
Globe wireframe visualization.
Glot is a plotting library for Golang built on top of gnuplot.
Deep Neural Networks for Golang (powered by MXNet)
Fast and convenient feature processing for low latency machine learning in Go.
Genetic Algorithms library written in Go / Golang. **[Deprecated]**
Go library to handle geometries.
Graph library for Go/Golang language. **[Deprecated]**
Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution. **[Deprecated]**
— benchmarks of machine learning inference for Go.
An open source Go transpiler for machine learning models.
Go binding for MXNet cpredictapi to do inference with a pre-trained model.
In-memory n-gram index with compression. *[Deprecated]*
A native Go clean room implementation of the Porter Stemming algorithm. **[Deprecated]**
Pattern recognition package in Go lang. **[Deprecated]**
Neural Networks written in Go.
Package for computer vision using OpenCV 4 and beyond.
Spherical geometry in Go.
A reinforcement learning library.
Machine learning for Go.
Machine learning library written in pure Go.
GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN. **[Deprecated]**
General-purpose graph library.
A linear algebra package for Go.
Implementations of optimization algorithms.
A plotting library.
A statistics library.
Deep learning in Go.
A high-level machine learning library in the vein of Keras.
An offline recommender system backend based on collaborative filtering written in Go.
Dataframes.
A pure Go implementation of the prediction part of GBRTs, including XGBoost and LightGBM.
Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). **[Deprecated]**
Golang implementation of the Paice/Husk Stemming Algorithm. *[Deprecated]*
Random forests implementation in Go. **[Deprecated]**
Golang implementation of Punkt sentence tokenizer.
Snowball Stemmer for Go.
Self-contained Machine Learning and Natural Language Processing library in Go.
PyTorch implementations of Stable Baselines (deep) reinforcement learning algorithms.
The Go Language library for SVG generation.
An embedded deep learning library for Go.
Word Embeddings: the full implementation of word2vec, GloVe in Go.
A DSL for deep neural networks. **[Deprecated]**
Haskell implementations of various ML algorithms. **[Deprecated]**
a suite of libraries for interpreting machine learning models according to their algebraic structure. **[Deprecated]**
Haskell Neural Network library.
Hopfield Networks for unsupervised learning in Haskell. **[Deprecated]**
Configurable Neural Networks in Haskell. **[Deprecated]**
A machine learning library by Airbnb designed from the ground up to be human friendly.
A Java Toolbox for Scalable Probabilistic Machine Learning.
Apache Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text.
A Java library for genetic algorithms, evolutionary computation, and stochastic local search, with a focus on self-adaptation / self-tuning, as well as parallel execution.
ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA. **[Deprecated]**
Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.
This project collects a number of core libraries for Natural Language Processing (NLP) developed in the University of Illinois' Cognitive Computation Group, for example `illinois-core-utilities` which provides a set of NLP-friendly data structures and a number of NLP-related utilities that support writing NLP applications, running experiments, etc, `illinois-edison` a library for feature extraction from illinois-core-utilities data structures and many other packages.
Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words.
Retina: an API performing complex NLP operations (disambiguation, classification, streaming text filtering, etc...) as quickly and intuitively as the brain.
Mathematics software for numeric computation, statistics, symbolic calculations, data analysis and data visualization.
Machine Learning framework for rapid development of Machine Learning and Statistical applications.
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning, designed to be easy to get started with and simple to use for Java developers.
Scalable deep learning for industry with parallel GPUs.
[Deprecated]**
Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trainings using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
Open source platform for distributed stream and batch data processing.
Distributed machine learning library in Flink.
ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
Hadoop/HDFS.
General Machine Learning library using Numenta’s Cortical Learning Algorithm.
a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
Real-time Query for Hadoop.
Cortical.io's FREE NLP, Retina API Analysis Tool (written in JavaFX!) - See the Tutorial Video.
Friendly guide on using Keras to implement a simple Neural Network in Python.
Just a simple implementation of K-Nearest Neighbors algorithm using with a bunch of similarity measures.
Learning Based Java is a modelling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application.
Java version of liblinear.
A tool kit for processing text using computational linguistics.
Distributed machine learning.
A Java-based package for statistical natural language processing, document classification, clustering, topic modelling, information extraction, and other machine learning applications to text.
An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
Distributed machine learning library in Spark.
Neuroph is lightweight Java neural network framework.
The NLP4J project provides software and resources for natural language processing. The project started at the Center for Computational Language and EducAtion Research, and is currently developed by the Center for Language and Information Research at Emory University. **[Deprecated]**
Distributed, masterless, high performance, fault tolerant data processing. Written entirely in Clojure.
A machine learning based toolkit for the processing of natural language text.
Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.
RankLib is a library of learning to rank algorithms. **[Deprecated]**
statistics, data mining and machine learning toolbox in Java.
RapidMiner integration into Java code.
SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms.
Statistical Machine Intelligence & Learning Engine.
Spark is a fast and general engine for large-scale data processing.
A classifier is a machine learning tool that will take data items and place them into one of k classes.
Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
Stanford NER is a Java implementation of a Named Entity Recognizer.
A natural language parser is a program that works out the grammatical structure of sentences.
A Part-Of-Speech Tagger (POS Tagger).
Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion.
SUTime is a library for recognizing and normalizing time expressions.
A tokenizer divides text into a sequence of tokens, which roughly correspond to "words".
Tokenization of raw text is a standard pre-processing step for many NLP tasks.
Storm is a distributed realtime computation system.
flexible, scalable machine learning (ML) language.
Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions").
A machine learning library written in Java by Oracle.
A Java implementation of Twitter's text processing library.
Weka is a collection of machine learning algorithms for data mining tasks.
Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file! **[Deprecated]**
Bayesian bandit implementation for Node and the browser. **[Deprecated]**
Neural networks in JavaScript **[Deprecated]**
Neural networks in JavaScript - continued community fork of Brain.
customizable library based on D3.js for easy chart drawing.
Platform for data visualization and analysis, using the visualizer project.
Agglomerative hierarchical clustering implemented in JavaScript for Node.js and the browser. **[Deprecated]**
Clustering algorithms implemented in JavaScript for Node.js and the browser. **[Deprecated]**
ConvNetJS is a JavaScript library for training Deep Learning models[DEEP LEARNING] **[Deprecated]**
Model Context Protocol server that exposes Creatify AI's video generation capabilities to AI assistants, enabling natural language video creation workflows.
Straight forward plotting built on D3. **[Deprecated]**
A lightweight framework for data analysis in JavaScript
Customizable SVG map/geo visualizations using D3.js. **[Deprecated]**
NodeJS Implementation of Decision Tree using ID3 Algorithm. **[Deprecated]**
Digital Neural Networks Architecture. **[Deprecated]**
K-means, fuzzy c-means and agglomerative clustering.
Unsupervised machine learning with multivariate Gaussian mixture model.
Library for calculating great circle distance.
Beer glass classifier created with Synaptic.
A fun TensorFlow.js-based oracle that tells, whether one wears a face mask or not. It can even tell when one wears the mask incorrectly.
Machine learning toolkit with classification and clustering for Node.js; supports visualization (see visualml.io).
Kalman filter for JavaScript. **[Deprecated]**
Run Keras models in the browser, with GPU support provided by WebGL 2.
Simple JavaScript implementation of the k-means algorithm, for node.js and the browser. **[Deprecated]**
JavaScript implementation of the k nearest neighbors algorithm for supervised learning.
A Natural Language Processor in JS.
LDA topic modelling for Node.js
JavaScript implementation of logistic regression/c4.5 decision tree **[Deprecated]**
Linear Regression library. **[Deprecated]**
Machine Learning library for the web, Node.js and developers
List of several machine learning libraries.
Machine learning and numerical analysis tools for Node.js and the Browser!
Friendly machine learning for the web!
MLPleaseHelp is a simple ML resource search engine. You can use this search engine right now at https://jgreenemi.github.io/MLPleaseHelp/, provided via GitHub Pages.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
General natural language facilities for node.
Visualizer for machine learning models.
C++ Neural Network library for Node.js. It has advantage on large dataset and multi-threaded training. **[Deprecated]**
built on top of the awesome d3 and Reactjs libraries
Natural Language processing in the browser.
An NLP library built in node over Natural, with entity extraction, sentiment analysis, automatic language identify, and so more.
FANN (Fast Artificial Neural Network Library) bindings for Node.js **[Deprecated]**
Support Vector Machine for Node.js
Indecent content checker with TensorFlow.js
Reinforcement learning using Markov Decision Processes.
A JavaScript application framework for machine learning and its engineering.
A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
Extensible system for analyzing and manipulating natural language.
Rock Paper Scissors trained in the browser with TensorFlow.js
Scientific and statistical computing in JavaScript. **[Deprecated]**
Node.js library with support for both simple and multiple linear regression. **[Deprecated]**
JavaScript library dedicated to graph drawing.
A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in Node.js.
Statistics kit for JavaScript. **[Deprecated]**
A standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
Vector and Matrix math for JavaScript. **[Deprecated]**
Architecture-free neural network library for Node.js and the browser.
A deep learning library for the browser, accelerated by WebGL and WebAssembly.
A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
Example of how the neural network learns to predict the angle between two points created with Synaptic.
A JavaScript implementation of Twitter's text processing library.
Fast Deep Neural Network JavaScript Framework. WebDNN uses next generation JavaScript API, WebGPU for GPU execution, and WebAssembly for CPU execution.
A new web standard that allows web apps and frameworks to accelerate deep neural networks with on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators.
Run XGBoost model and make predictions in Node.js.
Easily make interactive 3d plots built on Three.js **[Deprecated]**
library written on Vanilla JS for big data visualization.
Julia artificial neural networks. **[Deprecated]**
Multidimensional cluster generation in Julia.
Basic functions for clustering data: k-means, dp-means, etc.
A Julia package providing a variety of loaders for various NLP corpora.
Julia package for Regularized Discriminant Analysis.
Data structures that allow missing values. **[Deprecated]**
Metaprogramming tools for DataFrames.
Read files from Stata, SAS, and SPSS.
Reproducible data setup for reproducible science.
library for working with tabular data in Julia.
Decision Tree Classifier and Regressor.
Julia module for Distance evaluation.
A Julia package for probability distributions and associated functions.
Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
Functions and data dependencies for loading various word embeddings
Relax! Flux is the ML library that doesn't make you tensor
Crafty statistical graphics for Julia.
Julia package for Gaussian processes.
Large scale Gaussian Mixture Models.
Generalized linear models in Julia.
Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.
Graph layout algorithms in pure Julia.
Hypothesis tests for Julia.
An image library for Julia.
library for working with tabular data in Julia. **[Deprecated]**
Presentations for JuliaCon.
Kernel density estimators for Julia.
Koç University Deep Learning Framework.
Julia package for working with various human languages
Graph modelling and analysis.
Local regression, so smooooth!
Julia Machine Learning library. **[Deprecated]**
Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.
A Julia package for manifold learning and nonlinear dimensionality reduction.
MCMC tools for Julia. **[Deprecated]**
Flexible Deep Learning Framework in Julia.
A Julia package for fitting (statistical) mixed-effects models.
A set of functions to support the development of machine learning algorithms.
A Julia machine learning framework.
Deep Learning framework for Julia inspired by Caffe. **[Deprecated]**
Methods for dimensionality reduction.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
Simple Naive Bayes implementation in Julia. **[Deprecated]**
A neural network in Julia.
A Julia package for non-negative matrix factorization.
[Deprecated]**
A Julia framework for probabilistic graphical models.
Julia package for loading many of the data sets available in R.
Algorithms for regression analysis (e.g. linear regression and logistic regression). **[Deprecated]**
Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.
Basic sampling algorithms for Julia.
Julia implementation of the scikit-learn API.
Signal Processing tools for Julia.
basic MCMC sampler implemented in Julia. **[Deprecated]**
Statistical tests for Julia.
SVM for Julia. **[Deprecated]**
Julia package for text analysis.
Time series toolkit for Julia.
TopicModels for Julia. **[Deprecated]**
Tokenizers for Natural Language Processing in Julia
A Julia package for Princeton's WordNet
eXtreme Gradient Boosting Package in Julia.
Scripts to generate a dataset with static frames from the Arcade Learning Environment.
Autograd automatically differentiates native Torch code. Inspired by the original Python version.
Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy. **[Deprecated]**
.
Torch CUDA Neural Network Implementation.
Torch CUDA Implementation.
A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design patterns. **[Deprecated]**
Many useful features that aren't part of the main nn package.
A package for feature extraction in Torch. Provides SIFT and dSIFT modules. **[Deprecated]**
Graph package for Torch. **[Deprecated]**
An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images. **[Deprecated]**
A python package that integrates an LLM copilot inside the keras model development workflow.
KNN, kernel-weighted average, local linear regression smoothers. **[Deprecated]**
FFI Wrapper for liblbfgs. **[Deprecated]**
[Deprecated]**
A Lua wrapper around the Locality sensitive hashing library SHKit **[Deprecated]**
[Deprecated]**
A package to manipulate manifolds.
Neural Network package for Torch.
This package provides graphical computation for nn library in Torch7.
A completely unstable and experimental package that extends Torch's builtin nn library.
OpenGM is a C++ library for graphical modelling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM. **[Deprecated]**
An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
A state-of-the-art generic dense feature extractor. **[Deprecated]**
Numpy's randomkit, wrapped for Torch. **[Deprecated]**
A Recurrent Neural Network library that extends Torch's nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc.
code and tools around integral images. A library for finding interest points based on fast integral histograms. **[Deprecated]**
A bundle adjustment/structure from motion package. **[Deprecated]**
A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft.
Spaghetti (sparse linear) module for torch7 by @MichaelMathieu **[Deprecated]**
allows us to use hugin to stitch images and apply same stitching to a video sequence. **[Deprecated]**
Torch-SVM library. **[Deprecated]**
Scripts to load several popular datasets including:
framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming.
A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA). **[Deprecated]**
A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos. **[Deprecated]**
An old vowpalwabbit interface to torch. **[Deprecated]**
a simple and efficient end-to-end Automatic Speech Recognition (ASR) system from Facebook AI Research.
MATLAB code for bandlet transform.
A deep learning framework developed with cleanliness, readability, and speed in mind.
MATLAB source code that implements the contourlet transform and its utility functions.
Convolutional-Recursive Deep Learning for 3D Object Classification[DEEP LEARNING].
The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
A Library for Large Linear Classification.
A Library for Support Vector Machines.
Examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
Class on machine w/ PDF, lectures, code
MatlabBGL is a Matlab package for working with graphs.
Collection and a development kit of MATLAB mex functions for OpenCV library.
Multidimensional cluster generation in MATLAB/Octave.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
A NLP library for Matlab.
A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly with MATLAB.
A general-purpose MATLAB library for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found here.
This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.
A complete object-oriented environment for machine learning in Matlab.
MATLAB code for shearlet transform.
The spider is intended to be a complete object orientated environment for machine learning in Matlab.
An Open-Source SVM Library on GPUs and CPUs
It implemented 3 layers of neural networks ( Input Layer, Hidden Layer and Output Layer ) and it was named Back Propagation Neural Networks (BPN). This network can be used in products recommendation, user behavior analysis, data mining and data analysis. **[Deprecated]**
It implemented Fuzzy C-Means (FCM) the fuzzy clustering / classification algorithm on Machine Learning. It could be used in data mining and image compression. **[Deprecated]**
It is a non-supervisory and self-learning algorithm (adjust the weights) in the neural network of Machine Learning. **[Deprecated]**
It implemented K-Means clustering and classification algorithm. It could be used in data mining and image compression. **[Deprecated]**
An Objective-C multilayer perceptron library, with full support for training through backpropagation. Implemented using vDSP and vecLib, it's 20 times faster than its Java equivalent. Includes sample code for use from Swift.
Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural networks. It is built on top of the Apple's Accelerate Framework, using vectorized operations and hardware acceleration if available. **[Deprecated]**
It implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Networks (BPN) and designed unlimited-hidden-layers.
A Machine Learning framework for Objective-C and Swift (OS X / iOS).
A machine learning / bayesian inference assigning attributes to objects.
Chinese Words Segmentation Utilities.
Machine Learning library for PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
A library for machine learning that builds predictions using a linear regression.
A high-level machine learning (ML) library that lets you build programs that learn from data using the PHP language.
An Open Source Distributed Framework for Reinforcement Learning that makes build and train your agents easily.
> An easy-to-use & supercharged open-source AI metadata tracker.
А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
Code for Data Science at Olin College, Spring 2014.
Code repository for Think Bayes.
Code for Allen Downey's book Think Complexity.
Text and supporting code for Think OS: A Brief Introduction to Operating Systems.
A Python to Vega translator.
Approximate nearest neighbours implementation.
An Apache Incubating project for developing an open source machine learning library.
Machine Learning and Data Mining for Astronomy.
A community Python library for Astronomy.
: AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
: A tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
AutoViz performs automatic visualization of any dataset with a single line of Python code. Give it any input file (CSV, txt or JSON) of any size and AutoViz will visualize it. See Medium article.
Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
> Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal", is a comprehensive and scalable Python AutoML toolkit with imbalanced handling, ensembling, stacking and built-in feature selection. Featured in Medium article.
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
Book/iPython notebooks on Probabilistic Programming in Python.
A simple, but essential Bayesian optimization package, written in Python.
Bayesian Inference Tools in Python.
: Toolkit for package and deploy machine learning models for serving in production
topic modelling platform.
A library that contacts external servers.
Biologically-Inspired and Machine Learning Algorithms in Python. **[Deprecated]**
NumPy and Pandas interface to Big Data.
Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser). **[Deprecated]**
Interactive Web Plotting for Python.
Bolt Online Learning Toolbox. **[Deprecated]**
A dashboard library for interactive visualizations using flask socketio and react.
An API for plotting in Jupyter (IPython).
Fast, flexible and fun neural networks. This is the successor of PyBrain.
Theano based library for deep and recurrent neural networks.
An easy-to-use, Python-based feature store. Optimized for time-series data.
Convolutional autoencoders for 3D image/field compression applied to reduced order Data Assimilation.
A deep learning framework developed with cleanliness, readability, and speed in mind.
Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.
High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.
General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even multi-GPU) computation.
A web-based visualization and debugging platform for NuPIC. **[Deprecated]**
Flexible neural network framework.
a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some advanced bagging and boosting techniques such as gradient boosting, random forest and adaboost.
> A PyTorch based deep learning library for drug pair scoring
: The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
The Classical Language Toolkit.
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found here.
Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
: A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
Python binding to C++ library for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
: An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
Track, log, visualize and evaluate your LLM prompts and prompt chains.
: The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
as known as ``L0CV``, is a new generation of computer vision open source online learning media, a cross-platform interactive learning framework integrating graphics, source code and HTML. the L0CV ecosystem — Notebook, Datasets, Source Code, and from Diving-in to Advanced — as well as the L0CV Hub.
A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
Open source platform for deploying machine learning models in production.
Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree **[Deprecated]**
A flexible, fast recommender engine. **[Deprecated]**
A recommendation engine library for Python.
A plotting library for Python, based on D3.js.
A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.
A GitHub Repository Where you can Learn Datavisualizatoin Basics to Intermediate level.
Evolutionary algorithm framework.
A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
A PyTorch implementation of CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
: Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.
A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, Dlib and ArcFace.
DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.
conversational AI library with many pre-trained Russian NLP models.
Train and run a computer vision model with 5-10 lines of code.
FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. **[Deprecated]**
FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.
DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
A collection of image segmentation algorithms based on diffusion methods.
The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
Levenshtein and Hamming distance computation. **[Deprecated]**
"I learned Python by hacking first, and getting serious *later.* I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."
A deep learning-based translation library between 50 languages, built with `transformers`.
Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container. **[Deprecated]**
Tools for exploratory data analysis in Python.
A little logger for machine learning research. Output any object to the terminal, CSV, TensorBoard, text logs on disk, and more with just one call to `logger.log()`.
A PyTorch implementation of DeepDream. Allows individuals to quickly and easily train their own custom GoogleNet models with custom datasets for DeepDream.
Reading Wikipedia to answer open-domain questions.
High performance library for time series distances (DTW) and time series clustering.
fast implementation of edit distance.
A library for probabilistic modelling, inference, and criticism. Built on top of TensorFlow.
Deep learning operations reinvented (for pytorch, tensorflow, jax and others).
The Python ensemble sampling toolkit for affine-invariant MCMC.
ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.
: Eurybia monitors data and model drift over time and securizes model deployment with data validation.
: Interactive reports to analyze machine learning models during validation or production monitoring.
A fast Evolution Strategy implementation in Python.
face recognition system that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition and is easily deployed with docker.
Face recognition library that recognizes and manipulates faces from Python or from the command line.
High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
Open source library with an exhaustive battery of feature engineering and selection methods based on pandas and scikit-learn.
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
: An AutoML framework for the automated design of composite modelling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data (including multi-modal datasets).
Ignite your models into blazing-fast machine learning APIs with a modern framework.
This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.
: Frouros is an open source Python library for drift detection in machine learning systems.
Simple machine learning library, including Perceptron, Regression, Support Vector Machine, Decision Tree and more, it's easy to use and easy to learn for beginners.
Fuzzy String Matching in Python.
A toolkit for reproducible reinforcement learning research
A Chinese segment based on Conditional Random Field.
Topic Modelling for Humans.
Unified interface to ggplot2 popular R packages.
Same API as ggplot2 for R. **[Deprecated]**
A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.
A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
Library for calculating great circle distance.
Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
Gym4ReaL is a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a diverse set of tasks exposing RL algorithms to a variety of practical challenges.
A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).
Fundamentals of machine learning in python.
A framework for building industrial-strength applications with Transformer models and LLMs.
implementation of the hdbscan algorithm in Python - used for clustering
Given clinical parameters about a patient, can we predict whether or not they have heart disease?
GPU-Accelerated Deep Learning Library in Python. **[Deprecated]**
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit activeloop.ai for more info.
A service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.
> A delightful machine learning tool that allows you to train/fit, test and use models **without writing code**
binding to igraph library - General purpose graph library.
Implementation of image to image (pix2pix) translation from the paper by isola et al.[DEEP LEARNING]
Python toolbox for quick implementation, modification, evaluation, and visualization of ensemble learning algorithms for class-imbalanced data. Supports out-of-the-box multi-class imbalanced (long-tailed) classification.
Python module to perform under sampling and oversampling with various techniques.
Fast Python Collaborative Filtering for Implicit Datasets.
A library containing Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.
A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.
Notebooks and code for the book "Introduction to Machine Learning with Python"
IPython notebooks from Data School's video tutorials on scikit-learn.
Light face detection and recognition system with huge possibilities, based on Microsoft Face API and TensorFlow made for small IoT devices like raspberry pi.
The power of Chart.js in Jupyter Notebook.
JAX is Autograd and XLA, brought together for high-performance machine learning research.
a python library for doing approximate and phonetic matching of strings.
Chinese Words Segmentation Utilities.
An easier way to build neural search in the cloud. Compatible with Jupyter Notebooks.
Generative AI Image Toolset with GANs and Diffusion for Real-World Applications.
Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.
Amazon access control challenge.
Code for Kaggle Dogs vs. Cats competition.
Winning solution for the Galaxy Challenge on Kaggle.
A Kaggle competition: discriminate gender based on handwriting.
Kaggle Submission for "Detecting Insults in Social Commentary".
Merck challenge at Kaggle.
Predicting closed questions on Stack Overflow.
Code for Accelerometer Biometric Competition at Kaggle.
Predicting job salaries from ads - a Kaggle competition.
Code for the Best Buy competition at Kaggle.
Deep learning made easy.
Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet.
Code for the Kaggle acquire valued shoppers challenge.
Code for the Kaggle acquire valued shoppers challenge.
> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
Rendering beautiful SVG maps in Python.
High-level neural networks frontend for TensorFlow, CNTK and Theano.
An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.
A Python package for Korean natural language processing.
A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation via user-defined (Python) functions.
Lightweight library to build and train neural networks in Theano.
Energy-based machine learning models built upon PyTorch.
lifelines is a complete survival analysis library, written in pure Python
A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
Lightly is a computer vision framework for self-supervised learning.
Pretrain computer vision models on unlabeled data for industrial applications
A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.
Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.
> A graph sampling extension library for NetworkX with a Scikit-Learn like API.
Another Chinese segmentation library. **[Deprecated]**
Tensorflow and OpenAI Clarity's Lucid adapted for PyTorch.
automated build consisting of a web-interface, and set of programmatic-interface API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks.
: Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
A tensor-based framework for large-scale data computation which is often regarded as a parallel and distributed version of NumPy.
A Python 2D plotting library.
Application-oriented deep reinforcement learning framework addressing real-world decision problems.
An open source robotics benchmark for meta- and multi-task reinforcement learning
A Python module for metric learning.
> A distributed machine learning framework Apache Spark
: Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from Microsoft Research as well as from other companies and institutions.
Machine learning toolkit focused on supervised classification. **[Deprecated]**
Open Source framework to streamline use of neural networks.
– A minimal, educational, Pythonic implementation of autograd (~100 loc).
: An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.
Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.
Distributed machine learning library in Spark
MLX is an array framework for machine learning on Apple silicon, developed by Apple machine learning research.
A library consisting of useful tools for data science and machine learning tasks.
IPython notebooks for EEG/MEG data processing using mne-python.
A modular active learning framework for Python, built on top of scikit-learn.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
A Natural Adversarial Language Processing framework built over Tensorflow.
: Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
Blazing fast, lightweight and customizable fuzzy and semantic text search in Python with fuzzywuzzy/thefuzz compatible API.
Nervana's high-performance Python-based Deep Learning framework [DEEP LEARNING]. **[Deprecated]**
IPython notebooks for a complete course around understanding Nervana's Neon.
neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
A high-productivity software for complex networks.
Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].
A PyTorch implementation of DeepDream.
A PyTorch implementation of Justin Johnson's neural-style (neural style transfer).
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. **[Deprecated]**
: A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. **[Deprecated]**
Named-entity recognition using neural networks providing state-of-the-art-results
Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful.
Machine learning for NeuroImaging in Python.
A leading platform for building Python programs to work with human language data.
nn_builder is a python package that lets you build neural networks in 1 line
Python JIT (just in time) compiler to LLVM aimed at scientific Python by the developers of Cython and NumPy.
A fundamental package for scientific computing with Python.
: Reference implementations of ML models written in numpy
Numenta Platform for Intelligent Computing.
An all-in-one NuPIC Hierarchical Temporal Memory visualization and debugging super-tool! **[Deprecated]**
Natural language Understanding Toolkit. **[Deprecated]**
: A python machine learning library created to combine powefull data analasys features with tensors and machine learning components, while maintaining support for other libraries.
Business Intelligence (BI) in Python (Pandas web interface) **[Deprecated]**
> source code and experiments results for 2018 Data Science Bowl.
> source code and experiments results for Google AI Open Images - Object Detection Track.
> source code and experiments results for Home Credit Default Risk.
> source code and experiments results for TGS Salt Identification Challenge.
> source code and experiments results for Airbus Ship Detection Challenge.
> source code for Toxic Comment Classification Challenge.
> source code and experiments results for Santander Value Prediction Challenge.
Free and open source face recognition with deep neural networks.
A PyTorch-based framework to train and validate the models producing high-quality embeddings.
A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Open source computer vision API based on open source models.
A Python-inspired implementation of the Optimum-Path Forest classifier.
: Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
: Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.
A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search.
Examples demonstrating how to use Optunity in synergy with machine learning libraries.
Python-based meta-heuristic optimization techniques.
Open source data visualization and data analysis for novices and experts.
A library providing high-performance, easy-to-use data structures and data analysis tools.
Recipes for using Python's pandas library.
A general-purpose Python library for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found here.
Parris, the automated infrastructure setup tool for machine learning algorithms.
Simple, realtime visualization of neural network training performance.
A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
Web mining module for Python.
Open source Python module for computer vision. **[Deprecated]**
Python Environment for Bayesian Learning. **[Deprecated]**
Tools for writing, submitting, debugging, and monitoring Storm topologies in pure Python.
A python library for working with Probabilistic Graphical Models.
A better version of Jieba, developed by Peking University.
Collaborative web plotting for Python and matplotlib.
Multilingual text (NLP) processing toolkit.
Hidden Markov Models for Python, implemented in Cython for speed and efficiency.
comprehensive online course about using XGBoost in Python.
Build tool for data science pipelines.
Fast and automated time series forecasting framework by Facebook.
Another Python Machine Learning Library.
Algorithmic Trading with Machine Learning.
[Deprecated]**
Multidimensional cluster generation in Python.
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Deep Learning In Python. **[Deprecated]**
Simple plotting for Python. Wrapper for D3xterjs; easily render charts in-browser.
Short for Python Dynamics, used to assist with workflow in the modelling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
Genetic algorithm framework. **[Deprecated]**
A Python SVG Charts Creator.
Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft
library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
A Machine Learning library based on Theano. **[Deprecated]**
Markov Chain Monte Carlo sampling toolkit.
Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.
A pure-python graphics and GUI library built on PyQt4 / PySide and NumPy.
Python package that implements a novel white-box machine learning model for text classification, called SS3. Since SS3 has the ability to visually explain its rationale, this package also comes with easy-to-use interactive visualizations tools (online demos).
Python interface for converting Penn Treebank trees to Stanford Dependencies.
A Python library for secure and private Deep Learning built on PyTorch and TensorFlow.
A Python library for secure and private Deep Learning.
Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for Google's Tesseract-OCR Engine.
Course for Python programming for the Humanities, assuming no prior knowledge. Heavy focus on text processing / NLP.
Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
A Python library for implementing a Recommender System.
A Python extension module wrapping the full TiMBL C++ programming interface. Timbl is an elaborate k-Nearest Neighbours machine learning toolkit.
Python binding to ucto (a unicode-aware rule-based tokenizer for various languages).
Python bindings for ZPar, a statistical part-of-speech-tagger, constituency parser, and dependency parser for English.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
> A Modular Framework for Multi-Modal Tabular Learning.
> Graph Neural Network Library for PyTorch.
> A temporal extension of PyTorch Geometric for dynamic graph representation learning.
The lightweight PyTorch wrapper for high-performance AI research.
Toolbox of models, callbacks, and datasets for AI/ML researchers.
A PyTorch-Based Framework for Deep Learning in Computer Vision.
A python framework to transform natural language questions to queries in a database query language.
A "machine learning framework to automate text-and voice-based conversations."
an IPython-based environment for conducting data-driven research in a consistent and reproducible way. REP is not trying to substitute scikit-learn, but extends it and provides better user experience. **[Deprecated]**
Restricted Boltzmann Machines in Python. [DEEP LEARNING]
deep learning based cutting-edge facial detector for Python coming with facial landmarks
Retro Games in Gym
> A general purpose recommender metrics library for fair evaluation.
Python bindings for Regularized Greedy Forest (Tree) Library.
: A framework for general purpose online machine learning.
RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
Open-source software for robot simulation, integrated with OpenAI Gym.
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
Text processing tools and wrappers (e.g. Vowpal Wabbit)
Computation Pipeline library for python.
Topic Modelling the Sarah Palin emails.
A collection of algorithms for image processing in Python.
A Python module for machine learning built on top of SciPy.
Series of notebooks for learning scikit-learn.
A machine learning framework for multi-output/multi-label and stream data.
Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
A visualization library for quick and easy generation of common plots in data analysis and machine learning.
scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.
A Python-based ecosystem of open-source software for mathematics, science, and engineering.
SciPy tutorials. This is outdated, check out scipy-lecture-notes.
A python visualization library based on matplotlib.
A TensorFlow Keras-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images.
A PyTorch-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images.
Tweets Sentiment Analyzer
Sentiment classifier using word sense disambiguation.
PyTorch library for creating and training sequence autoencoders in just two lines of code
Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
: Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.
> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
The Shogun Machine Learning Toolbox.
Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
Python package for Bayesian Machine Learning with scikit-learn API.
: A Python library for Bayesian Evidential Learning (BEL) in order to estimate the uncertainty of a prediction.
Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
: An AutoML package for hyperparameters tuning using evolutionary algorithms, with built-in callbacks, plotting, remote logging and more.
A wrapper around scikit-learn that makes it simpler to conduct experiments.
A scikit-learn compatible neural network library that wraps PyTorch.
Skrub is a Python library that eases preprocessing and feature engineering for machine learning on dataframes.
A unified framework for machine learning with time series
Modular Deep Reinforcement Learning framework in PyTorch.
Natural Language Understanding library for intent classification and entity extraction
A library for processing Chinese text.
Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters, has python API.
Self Organizing Map written in Python (Uses neural networks for data analysis).
Industrial strength NLP with Python and Cython.
A library for email Spam filtering built on top of NLTK
Pandas on PySpark (POPS).
Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012. **[Deprecated]**
An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning
Implementation of machine learning stacking technique as a handy library in Python.
Python wrapper for Stanford CoreNLP **[Deprecated]**
Statistical modelling and econometrics in Python.
: Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.
> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
: Streamlit is an framework to create beautiful data apps in hours, not weeks.
Machine Learning for RC Cars.
A data exploration platform designed to be visual, intuitive, and interactive.
A scikit for building and analyzing recommender systems.
Interactive SVM Explorer, using Dash and scikit-learn
A Python library for symbolic mathematics.
Multidimensional synthetic data generation in Python.
TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
Open source software library for numerical computation using data flow graphs.
A federated learning framework for machine learning and other computations on decentralized data.
Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes such as machine learning training.
higher-level NLP built on Spacy.
Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
Text preprocessing package for use in NLP tasks.
TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs).
Deep learning library featuring a higher-level API for TensorFlow.
Machine Learning Prediction System on AWS Lambda
This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.
Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python.
Book on Bayesian Analysis.
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
A library that makes downloading publicly available neuromorphic datasets a breeze and provides event-based data transformation/augmentation pipelines.
Interactive topic model visualization/interpretation framework.
Topic modelling toolkit. **[Deprecated]**
A Python module that encode unstructured data into embeddings.
Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.
Variety of supported types of Artificial Neural Network and learning algorithms.
Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
Blazing fast short-text-topic-modelling for Python.
: Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features.
A high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Documentation can be found here.
Python bindings for the VIGRA C++ computer vision library.
GPU-based high-performance interactive OpenGL 2D/3D data visualization library.
A python package for data exploration and data analysis. **[Deprecated]**
ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle.
A Python Framework for Wind Energy Analysis and Prediction.
Predicting wine quality.
> Fast and easy-to-use backpropagation tool.
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
Python bindings for eXtreme Gradient Boosting (Tree) Library.
A high performance, easy-to-use, and scalable machine 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 advertisement and recommender systems.
A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora. **[Deprecated]**
Transcode sentence (or other sequence) to list of word vector.
A Pythonic algorithmic trading library.
ahaz: Regularization for semiparametric additive hazards regression. **[Deprecated]**
arules: Mining Association Rules and Frequent Itemsets
biglasso: Extending Lasso Model Fitting to Big Data in R.
bmrm: Bundle Methods for Regularized Risk Minimization Package.
Boruta: A wrapper algorithm for all-relevant feature selection.
bst: Gradient Boosting.
C50: C5.0 Decision Trees and Rule-Based Models.
Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models. **[Deprecated]**
General purpose gradient boosting on decision trees library with categorical features support out of the box for R.
Multidimensional cluster generation in R.
CORElearn: Classification, regression, feature evaluation and ordinal evaluation.
Cubist: Rule- and Instance-Based Regression Modelling.
`data.table` provides a high-performance version of base R’s `data.frame` with syntax and feature enhancements for ease of use, convenience and programming speed.
A data manipulation package that helps to solve the most common data manipulation problems.
e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
earth: Multivariate Adaptive Regression Spline Models
elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA.
ElemStatLearn: Data sets, functions and examples from the book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman.
evtree: Evolutionary Learning of Globally Optimal Trees.
forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package.
fpc: Flexible procedures for clustering.
frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks. **[Deprecated]**
GAMBoost: Generalized linear and additive models by likelihood based boosting. **[Deprecated]**
gamboostLSS: Boosting Methods for GAMLSS.
gbm: Generalized Boosted Regression Models.
A data visualization package based on the grammar of graphics.
glmnet: Lasso and elastic-net regularized generalized linear models.
glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
GMMBoost: Likelihood-based Boosting for Generalized mixed models. **[Deprecated]**
grplasso: Fitting user specified models with Group Lasso penalty.
grpreg: Regularization paths for regression models with grouped covariates.
A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM.
hda: Heteroscedastic Discriminant Analysis. **[Deprecated]**
binding to igraph library - General purpose graph library.
ipred: Improved Predictors.
kernlab: Kernel-based Machine Learning Lab.
klaR: Classification and visualization.
L0Learn: Fast algorithms for best subset selection.
lars: Least Angle Regression, Lasso and Forward Stagewise. **[Deprecated]**
lasso2: L1 constrained estimation aka ‘lasso’.
LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library.
LogicReg: Logic Regression.
maptree: Mapping, pruning, and graphing tree models. **[Deprecated]**
mboost: Model-Based Boosting.
medley: Blending regression models, using a greedy stepwise approach.
mlr: Machine Learning in R.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
ncvreg: Regularization paths for SCAD- and MCP-penalized regression models.
nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models. **[Deprecated]**
A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly to R.
pamr: Pam: prediction analysis for microarrays. **[Deprecated]**
party: A Laboratory for Recursive Partitioning
partykit: A Toolkit for Recursive Partitioning.
penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model.
penalizedLDA: Penalized classification using Fisher's linear discriminant. **[Deprecated]**
penalizedSVM: Feature Selection SVM using penalty functions.
quantregForest: Quantile Regression Forests.
randomForest: Breiman and Cutler's random forests for classification and regression.
randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
rattle: Graphical user interface for data mining in R.
rda: Shrunken Centroids Regularized Discriminant Analysis.
rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces. **[Deprecated]**
REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data. **[Deprecated]**
relaxo: Relaxed Lasso. **[Deprecated]**
rgenoud: R version of GENetic Optimization Using Derivatives
Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R.
rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression. **[Deprecated]**
ROCR: Visualizing the performance of scoring classifiers. **[Deprecated]**
RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories. **[Deprecated]**
rpart: Recursive Partitioning and Regression Trees.
RPMM: Recursively Partitioned Mixture Model.
RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS).
RWeka: R/Weka interface.
RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.
sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection. **[Deprecated]**
is the basis for truly interactive displays and dashboards in R. However, some measure of interactivity can be achieved with htmlwidgets bringing javascript libraries to R. These include, plotly, dygraphs, highcharter, and several others.
spectralGraphTopology: Learning Graphs from Data via Spectral Constraints.
Multi-algorithm ensemble learning packages.
svmpath: svmpath: the SVM Path algorithm. **[Deprecated]**
Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modelling and Reporting (AMR).
tgp: Bayesian treed Gaussian process models. **[Deprecated]**
and quanteda are the main packages for managing, analyzing, and visualizing textual data.
for visualizing geospatial data with static maps and leaflet for interactive maps
tree: Classification and regression trees.
varSelRF: Variable selection using random forests.
R binding for eXtreme Gradient Boosting (Tree) Library.
[Deprecated]**
Curated list of ML related resources for Ruby.
Curated link list for practical natural language processing in Ruby.
A general classifier module to allow Bayesian and other types of classifications.
Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby. **[Deprecated]**
A data management tool for humans. **[Deprecated]**
JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. **[Deprecated]**
Community based data collection, packed in gem. Get list of pretty much anything (stop words, countries, non words) in txt, JSON or hash. Demo/Search for a list
[Deprecated]**
A plotting library in Ruby built on top of Vega and D3. **[Deprecated]**
raspell is an interface binding for ruby. **[Deprecated]**
Ruby language bindings for LIBSVM which is a Library for Support Vector Machines.
Ruby - R bridge.
Some Machine Learning algorithms, implemented in Ruby. **[Deprecated]**
gnuplot wrapper for Ruby, especially for plotting ROC curves into SVG files. **[Deprecated]**
Rumale is a machine learning library in Ruby
Creates Random Forest classifiers from PMML files.
A beautiful graphing toolkit for Ruby.
Expose libstemmer_c to Ruby. **[Deprecated]**
Text Retrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.
A library that does auto linking and extraction of usernames, lists and hashtags in tweets.
Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing.
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals
Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use.
deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.
An open source machine learning framework in Rust Δ
Deep learning in Rust, with shape checked tensors and neural networks
Fast State-of-the-Art Tokenizers optimized for Research and Production
open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe. Available under the MIT license. [**[Deprecated]**](https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.s0a3uy4cc)
a comprehensive toolkit to build Machine Learning applications with Rust
`linfa` aims to provide a comprehensive toolkit to build Machine Learning applications with Rust
Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
A Rust library for Self Organising Maps (SOM).
a machine learning framework featuring logistic regression, support vector machines, decision trees and random forests.
RustNN is a feedforward neural network library. **[Deprecated]**
a pure-rust machine learning library.
"The Most Advanced Machine Learning Library In Rust."
Rust bindings for the C++ API of PyTorch
Add structure to unstructured text using a GUI.
Library of SAS Enterprise Miner process flow diagrams to help you learn by example about specific data mining topics.
Example code and materials that illustrate applications of SAS machine learning techniques.
Example code and materials that illustrate using neural networks with several hidden layers in SAS.
Example code and materials that illustrate techniques for integrating SAS with other analytics technologies in Java, PMML, Python and R.
Data mining and machine learning that creates deployable models using a GUI or code.
Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.
Concise cheat sheets containing machine learning best practices.
For conducting advanced statistical analysis.
Extract sentiment from text using a GUI.
Text mining using a GUI or code.
FREE! Includes all SAS packages necessary for data analysis and visualization, and includes online SAS courses.
Interactive, automated, and programmatic modelling with the latest machine learning algorithms in and end-to-end analytics environment, from data prep to deployment. Free trial available.
A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
Abstract Algebra for Scala.
CPU and GPU-accelerated Machine Learning Library.
CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.
Bioinformatics for the Scala programming language
Breeze is a numerical processing library for Scala.
Distributed decision tree ensemble learning in Scala.
Chalk is a natural language processing library. **[Deprecated]**
Scalable Machine Learning in Scalding.
Creating statically typed dynamic neural networks from object-oriented & functional programming constructs.
An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
Scala Library/REPL for Machine Learning Research.
FACTORIE is a toolkit for deployable probabilistic modelling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
a Scala library for constructing probabilistic models.
Open source platform for distributed stream and batch data processing.
Distributed machine learning library in Flink.
Scalding powered machine learning. **[Deprecated]**
H2O and Spark interoperability.
a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
> A distributed machine learning framework Apache Spark
Distributed machine learning library in Spark
Montague is a semantic parsing library for Scala with an easy-to-use DSL.
N-dimensional arrays in Scala 3. Think NumPy ndarray, but with compile-time type-checking/inference over shapes, tensor/axis labels & numeric data types
An ONNX (Open Neural Network eXchange) API and backend for typeful, functional deep learning in Scala (3).
PredictionIO, a machine learning server for software developers and data engineers.
Flexible Declarative Learning-Based Programming.
ScalaNLP is a suite of machine learning and numerical computing libraries.
A Scala API for Cascading.
Statistical Machine Intelligence and Learning Engine.
Natural language processing library built on top of Apache Spark ML to provide simple, performant, and accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment.
Interactive and Reactive Data Science using Scala and Spark.
Streaming MapReduce with Scalding and Storm.
Simply written algorithms to help study ML or write your own implementations.
Strongly-typed Scala API for TensorFlow.
Data management utilities for Scala. **[Deprecated]**
A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.
A curated list of machine learning models in CoreML format.
A curated list of pretrained CoreML models.
Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
The iOS and OS X neural network framework.
A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
A library for machine learning that builds predictions using a linear regression.
Highly optimized artificial intelligence and machine learning library written in Swift.
The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc...
a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.
A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development. **[Deprecated]**
Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. Supports LangGraph, CrewAI, n8n, OpenAI Agents, and more.
Ambrosia helps you clean up your LLM datasets using other LLMs.
Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure.
Model validation and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
Browser extension (Chrome and Firefox) that automatically finds and shows code implementations for machine learning papers anywhere: Google, Twitter, Arxiv, Scholar, etc.
ML powered analytics engine for outlier/anomaly detection and root cause analysis.
Chroma - the AI-native open-source embedding database
A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor their models in production.
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
Ready to use deeplearning docker images.
Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.
Python library for experiment metrics logging into simply formatted local files.
The all-in-one AI Observability and Security platform for responsible AI. It provides monitoring, analytics, and centralized controls to operationalize ML, GenAI, and LLM applications with trust. Fiddler helps enterprises scale LLM and ML deployments to deliver high performance AI, reduce costs, and be responsible in governance.
Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
– Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
Neural network inference from the command line
– Is an open source on-prem AI coding autonomous assistant that lives inside your repo, edits and tests files at SSD speed. Think Claude Code but with UI. plug in any LLM (OpenAI, Gemini, Ollama, etc.) and let it work for you.
A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
The agent simulation, evaluation, and observability platform helping product teams ship their AI applications with the quality and speed needed for real-world use.
– Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
Version and deploy your ML models following GitOps principles
platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
MLReef is an end-to-end development platform using the power of git to give structure and deep collaboration possibilities to the ML development process.
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and CPU/CUDA versions.
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
Vector database for applications that require real-time, scalable vector embedding and similarity search.
An online tool to generate boilerplate machine learning code that uses scikit-learn.
– Qdrant is open source vector similarity search engine with extended filtering support, written in Rust.
Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
Build semantic search applications and workflows.
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .
open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
Production AI plaftorm for deploying, managing, and observing any model at scale across any environment from cloud to edge. Let's go from python notebook to inferencing in minutes.
– Weaviate is an open source vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
More tools to improve the ML lifecycle: , . The following are GitHub-alike and targeting teams , , , , .