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CRFSharp is Conditional Random Fields implemented by .NET(C#), a machine learning algorithm for learning from labeled sequences of examples.



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This project is used to segment text into tokens according its context and semantic. the segment use front-maximum matching and CRF algorithms to split text.

pynlpl - PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotatation). The library is a divided into several packages and modules. It works on Python 2.7, as well as Python 3.

ABAGAIL - The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms

The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves. See Issues page.

Smile - Statistical Machine Intelligence & Learning Engine

Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby.

Machine Learning is a field of Computational Science - often nested under AI research - with many practical applications due to the ability of resulting algorithms to systematically implement a specific solution without explicit programmer's instructions. Obviously many algorithms need a definition of features to look at or a biggish training set of data to derive the solution from. This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.

H2O - Fast Scalable Machine Learning API For Smarter Applications

H2O is for data scientists and application developers who need fast, in-memory scalable machine learning for smarter applications. H2O is an open source parallel processing engine for machine learning. Unlike traditional analytics tools, H2O provides a combination of extraordinary math, a high performance parallel architecture, and unrivaled ease of use.

Apertium - A Language Independent Machine Translation Engine

Apertium is a machine translation platform, initially aimed at related-language pairs but expanded to deal with more divergent language pairs (such as English-Catalan). The platform provides a language-independent machine translation engine, tools to manage the linguistic data necessary to build a machine translation system for a given language pair and linguistic data for a growing number of language pairs.

keras-rl - Deep Reinforcement Learning for Keras.

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

Dclib - Portable C++ library

dlib is a library for developing portable applications dealing with networking, threads, graphical interfaces, data structures, linear algebra, machine learning, XML and text parsing, numerical optimization, Bayesian nets, data compression routines, linked lists, binary search trees, linear algebra and matrix utilities, machine learning algorithms, and many other general utilities.

Machine Learning Framework

Machine Learning Framework (MLF) is a library based on .NET Framework for machine learning implementation. This library consists of collection of machine learning algorithms such as Bayesian, Neural Network, SOM, Genetic Algorithm, SVM, and etc.

JSAT - Java Statistical Analysis Tool, a Java library for Machine Learning

JSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

Gate - General Architecture for Text Engineering

GATE excels at text analysis of all shapes and sizes. It provides support for diverse language processing tasks such as parsers, morphology, tagging, Information Retrieval tools, Information Extraction components for various languages, and many others. It provides support to measure, evaluate, model and persist the data structure. It could analyze text or speech. It has built-in support for machine learning and also adds support for different implementation of machine learning via plugin.

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

ml-agents - Unity Machine Learning Agents

Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

Java Machine Learning Library

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

Open Machine Learning

Open Machine Learning will be a collection of data structures and algorithms written in C# that enables machine learning research.

photon-ml - A scalable machine learning library on Apache Spark

New: check out our hands-on tutorial.Photon Machine Learning (Photon ML) is a machine learning library based upon Apache Spark originally developed by the LinkedIn Machine Learning Algorithms team.

Sensorbee - Lightweight stream processing engine for IoT

Sensorbee is designed for low-latency processing of streaming data at the edge of the network. IoT devices frequently generate large volumes of unstructured streaming data, such as video and audio streams. Even if the data streams are structured, they may be meaningless if their temporal characteristics are not considered. Cloud-based services are generally not good at processing these kinds of data. Preprocessing data streams before they are sent to the cloud makes large scale data processing in the cloud more efficient and reduces the usage of network bandwidth.

numl - Machine Learning for .NET

This library is designed to assist in the use of common Machine Learning Algorithms in conjunction with the .NET platform. It is designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models.I would love to take contributions! Please read this.

ai-resources - Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning

Update April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is ( led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.