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@ -357,7 +357,7 @@ Further resources:
* [Neanderthal](https://neanderthal.uncomplicate.org/) - Fast Clojure Matrix Library (native CPU, GPU, OpenCL, CUDA)
* [kixistats](https://github.com/MastodonC/kixi.stats) - A library of statistical distribution sampling and transducing functions
* [fastmath](https://github.com/generateme/fastmath) - A collection of functions for mathematical and statistical computing, macine learning, etc., wrapping several JVM libraries
* [matlib](https://github.com/atisharma/matlib) - a Clojure library of optimisation and control theory tools and convenience functions based on Neanderthal.
* [matlib](https://github.com/atisharma/matlib) - A Clojure library of optimisation and control theory tools and convenience functions based on Neanderthal.
<a name="clojure-extra"></a>
#### Extra
@ -514,7 +514,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Stanford SPIED](https://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion.
* [Twitter Text Java](https://github.com/twitter/twitter-text/tree/master/java) - A Java implementation of Twitter's text processing library.
* [MALLET](http://mallet.cs.umass.edu/) - A Java-based package for statistical natural language processing, document classification, clustering, topic modelling, information extraction, and other machine learning applications to text.
* [OpenNLP](https://opennlp.apache.org/) - a machine learning based toolkit for the processing of natural language text.
* [OpenNLP](https://opennlp.apache.org/) - A machine learning based toolkit for the processing of natural language text.
* [LingPipe](http://alias-i.com/lingpipe/index.html) - A tool kit for processing text using computational linguistics.
* [ClearTK](https://github.com/ClearTK/cleartk) - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA. **[Deprecated]**
* [Apache cTAKES](https://ctakes.apache.org/) - 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.
@ -547,7 +547,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Stanford Classifier](https://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes.
* [Smile](https://haifengl.github.io/) - Statistical Machine Intelligence & Learning Engine.
* [SystemML](https://github.com/apache/systemml) - flexible, scalable machine learning (ML) language.
* [Tribou](https://tribuo.org) - a machine learning library written in Java by Oracle.
* [Tribou](https://tribuo.org) - A machine learning library written in Java by Oracle.
* [Weka](https://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks.
* [LBJava](https://github.com/CogComp/lbjava) - 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.
* [knn-java-library](https://github.com/felipexw/knn-java-library) - Just a simple implementation of K-Nearest Neighbors algorithm using with a bunch of similarity measures.
@ -868,7 +868,7 @@ on MNIST digits[DEEP LEARNING].
* [Pattern Recognition and Machine Learning](https://github.com/PRML/PRMLT) - This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.
* [Optunity](https://optunity.readthedocs.io/en/latest/) - 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.
* [MXNet](https://github.com/apache/incubator-mxnet/) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [Machine Learning in MatLab/Octave](https://github.com/trekhleb/machine-learning-octave) - examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
* [Machine Learning in MatLab/Octave](https://github.com/trekhleb/machine-learning-octave) - Examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
<a name="matlab-data-analysis--data-visualization"></a>
@ -902,7 +902,7 @@ on MNIST digits[DEEP LEARNING].
* [DiffSharp](https://diffsharp.github.io/DiffSharp/) - 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.
* [Encog](https://www.nuget.org/packages/encog-dotnet-core/) - 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.
* [GeneticSharp](https://github.com/giacomelli/GeneticSharp) - 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](https://dotnet.github.io/infer/) - 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 to 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.
* [Infer.NET](https://dotnet.github.io/infer/) - 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.
* [ML.NET](https://github.com/dotnet/machinelearning) - 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.
* [Neural Network Designer](https://sourceforge.net/projects/nnd/) - 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.
* [Synapses](https://github.com/mrdimosthenis/Synapses) - Neural network library in F#.
@ -926,8 +926,8 @@ on MNIST digits[DEEP LEARNING].
* [MLPNeuralNet](https://github.com/nikolaypavlov/MLPNeuralNet) - 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]**
* [MAChineLearning](https://github.com/gianlucabertani/MAChineLearning) - 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.
* [BPN-NeuralNetwork](https://github.com/Kalvar/ios-BPN-NeuralNetwork) - 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]**
* [Multi-Perceptron-NeuralNetwork](https://github.com/Kalvar/ios-Multi-Perceptron-NeuralNetwork) - 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.
* [KRHebbian-Algorithm](https://github.com/Kalvar/ios-KRHebbian-Algorithm) - It is a non-supervisor and self-learning algorithm (adjust the weights) in the neural network of Machine Learning. **[Deprecated]**
* [Multi-Perceptron-NeuralNetwork](https://github.com/Kalvar/ios-Multi-Perceptron-NeuralNetwork) - 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.
* [KRHebbian-Algorithm](https://github.com/Kalvar/ios-KRHebbian-Algorithm) - It is a non-supervisory and self-learning algorithm (adjust the weights) in the neural network of Machine Learning. **[Deprecated]**
* [KRKmeans-Algorithm](https://github.com/Kalvar/ios-KRKmeans-Algorithm) - It implemented K-Means clustering and classification algorithm. It could be used in data mining and image compression. **[Deprecated]**
* [KRFuzzyCMeans-Algorithm](https://github.com/Kalvar/ios-KRFuzzyCMeans-Algorithm) - 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]**
@ -1127,7 +1127,7 @@ be
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python.
* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - A service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [Towhee](https://towhee.io) - A Python module that encode unstructured data into embeddings.
* [scikit-learn](https://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [metric-learn](https://github.com/metric-learn/metric-learn) - A Python module for metric learning.
@ -1445,7 +1445,7 @@ be
<a name="python-reinforcement-learning"></a>
#### Reinforcement Learning
* [DeepMind Lab](https://github.com/deepmind/lab) - 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.
* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - A library for developing and comparing reinforcement learning algorithms (successor of [gym](https://github.com/openai/gym).
* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).
* [Serpent.AI](https://github.com/SerpentAI/SerpentAI) - 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.
* [ViZDoom](https://github.com/mwydmuch/ViZDoom) - 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.
* [Roboschool](https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.