Tag Archives: Matlab

PART 1: MATLAB, NEURAL NETWORKS AND THE BTC-E TRADING API.


via Part 1: MATLAB, Neural Networks and the BTC-e trading API. | embedded thought.

Let’s get started:

This might contain around 5 parts, so here we go..

exampleANN

I’m in the beginning process of developing a trading-bot using Matlab. Right now the plan is to save data concerning what the order-books look like before and after a dump. This data will be used for training the Neural Network through identification using pattern recognition.

PART 2: MATLAB, NEURAL NETWORKS AND THE BTC-E TRADING API. ( DATA COLLECTION)


via PART 2: MATLAB, NEURAL NETWORKS AND THE BTC-E TRADING API. ( Data collection) | embedded thought.

I will not be going in to full detail on every step and I will be assuming you have a basic understanding of MATLAB. Feel free to leave a reply if you have any questions.

Scientific Computing with Hy: Linear Regressions


Click to Read

A tutorial adventure with Python 3, cytoolz, NumPy, and matplotlib

This tutorial was adapted from the Linear Regression tutorial for the Clojure scientific computing platform Incanter. It has been significantly modified to take advantage of the following, however:

  • Python 3
  • Hy – Clojure-Flavoured Python
  • cytoolz – a Cython implementation of the famous pytoolz suite
  • NumPy – a full-featured numeric library for Python
  • matplotlib – a Python mathematical plotting library, originally inspired by MATLAB

Okay, to be honest, we don’t really use cytoolz in the linear regression exercise; but we really wanted to 🙂 The opportunity just didn’t arise. Seriously, though, cytoolz some some pretty sweet stuff, and provides fast utility functions you may be missing if you’re coming (or returning!) to Python after living in the land of functional programming. In particular, it borrows heavily from the Clojure API (which is, quite bluntly, awesome).

A curated list of awesome Machine Learning frameworks, libraries and software.


via josephmisiti/awesome-machine-learning · GitHub.

A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php. Other awesome lists can be found in the awesome-awesomeness list.

If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti

For a list of free machine learning books available for download, go here

Table of Contents

C

General-Purpose Machine Learning

  • Recommender – A C library for product recommendations/suggestions using collaborative filtering (CF).
  • Accord-Framework -The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.

Computer Vision

  • CCV – C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

C++

Computer Vision

  • OpenCV – OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
  • DLib – DLib has C++ and Python interfaces for face detection and training general object detectors.
  • EBLearn – Eblearn is an object-oriented C++ library that implements various machine learning models *

General-Purpose Machine Learning

  • MLPack
  • DLib – A suite of ML tools designed to be easy to imbed in other applications
  • ecogg
  • shark
  • Vowpal Wabbit (VW) – A fast out-of-core learning system.
  • sofia-ml – Suite of fast incremental algorithms.
  • Shogun – The Shogun Machine Learning Toolbox
  • Caffe – A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
  • CUDA – This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
  • Stan – A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
  • BanditLib – A simple Multi-armed Bandit library.

Natural Language Processing

  • MIT Information Extraction Toolkit – C, C++, and Python tools for named entity recognition and relation extraction
  • CRF++ – Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.

Speech Recognition

  • Kaldi – Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.

Sequence Analysis

  • ToPS – This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.

Clojure

Natural Language Processing

  • Clojure-openNLP – Natural Language Processing in Clojure (opennlp)
  • Infections-clj – Rails-like inflection library for Clojure and ClojureScript

General-Purpose Machine Learning

  • Touchstone – Clojure A/B testing library
  • Clojush – he Push programming language and the PushGP genetic programming system implemented in Clojure
  • Infer – Inference and machine learning in clojure
  • Clj-ML – A machine learning library for Clojure built on top of Weka and friends
  • Encog – Clojure wrapper for Encog (v3) (Machine-Learning framework that specialises in neural-nets)
  • Fungp – A genetic programming library for Clojure
  • Statistiker – Basic Machine Learning algorithms in Clojure.
  • clortex – General Machine Learning library using Numenta’s Cortical Learning Algorithm
  • comportex – Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm

Data Analysis / Data Visualization

  • Incanter – Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
  • PigPen – Map-Reduce for Clojure.
  • Envision – Clojure Data Visualisation library, based on Statistiker and D3 ## Erlang

General-Purpose Machine Learning

  • Disco – Map Reduce in Erlang

Go

Natural Language Processing

  • go-porterstemmer – A native Go clean room implementation of the Porter Stemming algorithm.
  • paicehusk – Golang implementation of the Paice/Husk Stemming Algorithm.
  • snowball – Snowball Stemmer for Go.
  • go-ngram – In-memory n-gram index with compression.

General-Purpose Machine Learning

  • Go Learn – Machine Learning for Go
  • go-pr – Pattern recognition package in Go lang.
  • bayesian – Naive Bayesian Classification for Golang.
  • go-galib – Genetic Algorithms library written in Go / golang
  • Cloudforest – Ensembles of decision trees in go/golang.
  • gobrain – Neural Networks written in go

Data Analysis / Data Visualization

  • go-graph – Graph library for Go/golang language.
  • SVGo – The Go Language library for SVG generation

Haskell

General-Purpose Machine Learning

  • haskell-ml – Haskell implementations of various ML algorithms.
  • HLearn – a suite of libraries for interpreting machine learning models according to their algebraic structure.
  • hnn – Haskell Neural Network library.
  • hopfield-networks – Hopfield Networks for unsupervised learning in Haskell.

Java

Natural Language Processing

  • CoreNLP – 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
  • Stanford Parser – A natural language parser is a program that works out the grammatical structure of sentences
  • Stanford POS Tagger – A Part-Of-Speech Tagger (POS Tagger
  • Stanford Name Entity Recognizer – Stanford NER is a Java implementation of a Named Entity Recognizer.
  • Stanford Word Segmenter – Tokenization of raw text is a standard pre-processing step for many NLP tasks.
  • Tregex, Tsurgeon and Semgrex – 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”).
  • Stanford Phrasal: A Phrase-Based Translation System
  • Stanford English Tokenizer – Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
  • Stanford Tokens Regex – A tokenizer divides text into a sequence of tokens, which roughly correspond to “words”
  • Stanford Temporal Tagger – SUTime is a library for recognizing and normalizing time expressions.
  • Stanford SPIED – Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
  • Stanford Topic Modeling Toolbox – Topic modeling tools to social scientists and others who wish to perform analysis on datasets
  • Twitter Text Java – A Java implementation of Twitter’s text processing library
  • MALLET – A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
  • OpenNLP – a machine learning based toolkit for the processing of natural language text.
  • LingPipe – A tool kit for processing text using computational linguistics.
  • ClearTK – ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
  • Apache cTAKES – 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.

General-Purpose Machine Learning

  • JSAT – Numerous Machine Learning algoirhtms for classification, regresion, and clustering.
  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • Datumbox – Machine Learning framework for rapid development of Machine Learning and Statistical applications
  • Mahout – Distributed machine learning
  • Stanford Classifier – A classifier is a machine learning tool that will take data items and place them into one of k classes.
  • Weka – Weka is a collection of machine learning algorithms for data mining tasks
  • Meka – An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
  • ORYX – Simple real-time large-scale machine learning infrastructure.
  • H2O – ML engine that supports distributed learning on data stored in HDFS.
  • WalnutiQ – object oriented model of the human brain
  • ELKI – Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
  • Neuroph – Neuroph is lightweight Java neural network framework
  • java-deeplearning – Distributed Deep Learning Platform for Java, Clojure,Scala
  • htm.java – General Machine Learning library using Numenta’s Cortical Learning Algorithm

Speech Recognition

  • CMU Sphinx – Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.

Data Analysis / Data Visualization

  • Hadoop – Hadoop/HDFS
  • Spark – Spark is a fast and general engine for large-scale data processing.
  • Impala – Real-time Query for Hadoop

Javascript

Natural Language Processing

  • Twitter-text-js – A JavaScript implementation of Twitter’s text processing library
  • NLP.js – NLP utilities in javascript and coffeescript
  • natural – General natural language facilities for node
  • Knwl.js – A Natural Language Processor in JS
  • Retext – Extensible system for analysing and manipulating natural language

Data Analysis / Data Visualization

General-Purpose Machine Learning

  • Convnet.js – ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]
  • Clustering.js – Clustering algorithms implemented in Javascript for Node.js and the browser
  • Decision Trees – NodeJS Implementation of Decision Tree using ID3 Algorithm
  • Node-fann – FANN (Fast Artificial Neural Network Library) bindings for Node.js
  • Kmeans.js – Simple Javascript implementation of the k-means algorithm, for node.js and the browser
  • LDA.js – LDA topic modeling for node.js
  • Learning.js – Javascript implementation of logistic regression/c4.5 decision tree
  • Machine Learning – Machine learning library for Node.js
  • Node-SVM – Support Vector Machine for nodejs
  • Brain – Neural networks in JavaScript
  • Bayesian-Bandit – Bayesian bandit implementation for Node and the browser.
  • Synaptic – Architecture-free neural network library for node.js and the browser

Julia

General-Purpose Machine Learning

  • PGM – A Julia framework for probabilistic graphical models.
  • DA – Julia package for Regularized Discriminant Analysis
  • Regression – Algorithms for regression analysis (e.g. linear regression and logistic regression)
  • Local Regression – Local regression, so smooooth!
  • Naive Bayes – Simple Naive Bayes implementation in Julia
  • Mixed Models – A Julia package for fitting (statistical) mixed-effects models
  • Simple MCMC – basic mcmc sampler implemented in Julia
  • Distance – Julia module for Distance evaluation
  • Decision Tree – Decision Tree Classifier and Regressor
  • Neural – A neural network in Julia
  • MCMC – MCMC tools for Julia
  • GLM – Generalized linear models in Julia
  • Online Learning
  • GLMNet – Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
  • Clustering – Basic functions for clustering data: k-means, dp-means, etc.
  • SVM – SVM’s for Julia
  • Kernal Density – Kernel density estimators for julia
  • Dimensionality Reduction – Methods for dimensionality reduction
  • NMF – A Julia package for non-negative matrix factorization
  • ANN – Julia artificial neural networks

Natural Language Processing

Data Analysis / Data Visualization

  • Graph Layout – Graph layout algorithms in pure Julia
  • Data Frames Meta – Metaprogramming tools for DataFrames
  • Julia Data – library for working with tabular data in Julia
  • Data Read – Read files from Stata, SAS, and SPSS
  • Hypothesis Tests – Hypothesis tests for Julia
  • Gadfly – Crafty statistical graphics for Julia.
  • Stats – Statistical tests for Julia
  • RDataSets – Julia package for loading many of the data sets available in R
  • DataFrames – library for working with tabular data in Julia
  • Distributions – A Julia package for probability distributions and associated functions.
  • Data Arrays – Data structures that allow missing values
  • Time Series – Time series toolkit for Julia
  • Sampling – Basic sampling algorithms for Julia

Misc Stuff / Presentations

  • DSP – Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
  • JuliaCon Presentations – Presentations for JuliaCon
  • SignalProcessing – Signal Processing tools for Julia
  • Images – An image library for Julia

Lua

General-Purpose Machine Learning

  • Torch7
    • cephes – 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.
    • graph – Graph package for Torch
    • randomkit – Numpy’s randomkit, wrapped for Torch
    • signal – A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft
    • nn – Neural Network package for Torch
    • nngraph – This package provides graphical computation for nn library in Torch7.
    • nnx – A completely unstable and experimental package that extends Torch’s builtin nn library
    • optim – An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
    • unsup – A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, …), and self-contained algorithms (k-means, PCA).
    • manifold – A package to manipulate manifolds
    • svm – Torch-SVM library
    • lbfgs – FFI Wrapper for liblbfgs
    • vowpalwabbit – An old vowpalwabbit interface to torch.
    • OpenGM – OpenGM is a C++ library for graphical modeling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.
    • sphagetti – Spaghetti (sparse linear) module for torch7 by @MichaelMathieu
    • LuaSHKit – A lua wrapper around the Locality sensitive hashing library SHKit
    • kernel smoothing – KNN, kernel-weighted average, local linear regression smoothers
    • cutorch – Torch CUDA Implementation
    • cunn – Torch CUDA Neural Network Implementation
    • imgraph – 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.
    • videograph – 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.
    • saliency – code and tools around integral images. A library for finding interest points based on fast integral histograms.
    • stitch – allows us to use hugin to stitch images and apply same stitching to a video sequence
    • sfm – A bundle adjustment/structure from motion package
    • fex – A package for feature extraction in Torch. Provides SIFT and dSIFT modules.
    • OverFeat – A state-of-the-art generic dense feature extractor
  • Numeric Lua
  • Lunatic Python
  • SciLua
  • Lua – Numerical Algorithms
  • Lunum

Demos and Scripts

  • Core torch7 demos repository.
    • linear-regression, logistic-regression
    • face detector (training and detection as separate demos)
    • mst-based-segmenter
    • train-a-digit-classifier
    • train-autoencoder
    • optical flow demo
    • train-on-housenumbers
    • train-on-cifar
    • tracking with deep nets
    • kinect demo
    • filter-bank visualization
    • saliency-networks
  • Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo)
  • Music Tagging – Music Tagging scripts for torch7
  • torch-datasets – Scripts to load several popular datasets including:
    • BSR 500
    • CIFAR-10
    • COIL
    • Street View House Numbers
    • MNIST
    • NORB
  • Atari2600 – Scripts to generate a dataset with static frames from the Arcade Learning Environment

Matlab

Computer Vision

  • Contourlets – MATLAB source code that implements the contourlet transform and its utility functions.
  • Shearlets – MATLAB code for shearlet transform
  • Curvelets – The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
  • Bandlets – MATLAB code for bandlet transform

Natural Language Processing

  • NLP – An NLP library for Matlab

General-Purpose Machine Learning

Data Analysis / Data Visualization

  • matlab_gbl – MatlabBGL is a Matlab package for working with graphs.
  • gamic – Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL’s mex functions.

.NET

Computer Vision

  • OpenCVDotNet – A wrapper for the OpenCV project to be used with .NET applications.
  • Emgu CV – Cross platform wrapper of OpenCV which can be compiled in Mono to e run on Windows, Linus, Mac OS X, iOS, and Android.

Natural Language Processing

  • Stanford.NLP for .NET – A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.

General-Purpose Machine Learning

  • Accord.MachineLearning – 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.
  • Vulpes – Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
  • Encog – 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.
  • Neural Network Designer – 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 feed back. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.

Data Analysis / Data Visualization

  • numl – numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
  • Math.NET Numerics – Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day 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.
  • Sho – 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.

Objective C

General-Purpose Machine Learning

  • MLPNeuralNet – Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple’s Accelerate Framework, using vectorized operations and hardware acceleration if available.

Python

Computer Vision

  • SimpleCV – 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.

Natural Language Processing

  • NLTK – A leading platform for building Python programs to work with human language data.
  • Pattern – A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
  • Quepy – A python framework to transform natural language questions to queries in a database query language
  • TextBlob – 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.
  • YAlign – A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora.
  • jieba – Chinese Words Segmentation Utilities.
  • SnowNLP – A library for processing Chinese text.
  • loso – Another Chinese segmentation library.
  • genius – A Chinese segment base on Conditional Random Field.
  • nut – Natural language Understanding Toolkit
  • Rosetta – Text processing tools and wrappers (e.g. Vowpal Wabbit)

General-Purpose Machine Learning

  • Bayesian Methods for Hackers – Book/iPython notebooks on Probabilistic Programming in Python
  • Featureforge A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • scikit-learn – A Python module for machine learning built on top of SciPy.
  • SimpleAI Python implementation of many of the artificial intelligence algorithms described on the book “Artificial Intelligence, a Modern Approach”. It focuses on providing an easy to use, well documented and tested library.
  • astroML – Machine Learning and Data Mining for Astronomy.
  • graphlab-create – A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
  • BigML – A library that contacts external servers.
  • pattern – Web mining module for Python.
  • NuPIC – Numenta Platform for Intelligent Computing.
  • Pylearn2 – A Machine Learning library based on Theano.
  • hebel – GPU-Accelerated Deep Learning Library in Python.
  • gensim – Topic Modelling for Humans.
  • PyBrain – Another Python Machine Learning Library.
  • Crab – A flexible, fast recommender engine.
  • python-recsys – A Python library for implementing a Recommender System.
  • thinking bayes – Book on Bayesian Analysis
  • Restricted Boltzmann Machines -Restricted Boltzmann Machines in Python. [DEEP LEARNING]
  • Bolt – Bolt Online Learning Toolbox
  • CoverTree – Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
  • nilearn – Machine learning for NeuroImaging in Python
  • Shogun – The Shogun Machine Learning Toolbox
  • Pyevolve – Genetic algorithm framework.
  • Caffe – A deep learning framework developed with cleanliness, readability, and speed in mind.
  • breze – Theano based library for deep and recurrent neural networks
  • pyhsmm – 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.
  • mrjob – A library to let Python program run on Hadoop.
  • SKLL – A wrapper around scikit-learn that makes it simpler to conduct experiments.
  • neurolabhttps://code.google.com/p/neurolab/
  • Spearmint – 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.

Data Analysis / Data Visualization

  • SciPy – A Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • NumPy – A fundamental package for scientific computing with Python.
  • Numba – Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
  • NetworkX – A high-productivity software for complex networks.
  • Pandas – A library providing high-performance, easy-to-use data structures and data analysis tools.
  • Open Mining – Business Intelligence (BI) in Python (Pandas web interface)
  • PyMC – Markov Chain Monte Carlo sampling toolkit.
  • zipline – A Pythonic algorithmic trading library.
  • PyDy – Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
  • SymPy – A Python library for symbolic mathematics.
  • statsmodels – Statistical modeling and econometrics in Python.
  • astropy – A community Python library for Astronomy.
  • matplotlib – A Python 2D plotting library.
  • bokeh – Interactive Web Plotting for Python.
  • plotly – Collaborative web plotting for Python and matplotlib.
  • vincent – A Python to Vega translator.
  • d3py – A plottling library for Python, based on D3.js.
  • ggplot – Same API as ggplot2 for R.
  • Kartograph.py – Rendering beautiful SVG maps in Python.
  • pygal – A Python SVG Charts Creator.
  • PyQtGraph – A pure-python graphics and GUI library built on PyQt4 / PySide and NumPy.
  • pycascading
  • Petrel – Tools for writing, submitting, debugging, and monitoring Storm topologies in pure Python.
  • Blaze – NumPy and Pandas interface to Big Data.
  • emcee – The Python ensemble sampling toolkit for affine-invariant MCMC.
  • windML – A Python Framework for Wind Energy Analysis and Prediction
  • vispy – GPU-based high-performance interactive OpenGL 2D/3D data visualization library
  • cerebro2 A web-based visualization and debugging platform for NuPIC.
  • NuPIC Studio An all-in-one NuPIC Hierarchical Temporal Memory visualization and debugging super-tool!

Misc Scripts / iPython Notebooks / Codebases

Kaggle Competition Source Code

Ruby

Natural Language Processing

  • Treat – Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
  • Ruby Linguistics – Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independant front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
  • Stemmer – Expose libstemmer_c to Ruby
  • Ruby Wordnet – This library is a Ruby interface to WordNet
  • Raspel – raspell is an interface binding for ruby
  • UEA Stemmer – Ruby port of UEALite Stemmer – a conservative stemmer for search and indexing
  • Twitter-text-rb – A library that does auto linking and extraction of usernames, lists and hashtags in tweets

General-Purpose Machine Learning

Data Analysis / Data Visualization

  • rsruby – Ruby – R bridge
  • data-visualization-ruby – Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby
  • ruby-plot – gnuplot wrapper for ruby, especially for plotting roc curves into svg files
  • plot-rb – A plotting library in Ruby built on top of Vega and D3.
  • scruffy – A beautiful graphing toolkit for Ruby
  • SciRuby
  • Glean – A data management tool for humans
  • Bioruby
  • Arel

Misc

R

General-Purpose Machine Learning

  • h2o – A framework for fast, parallel, and distributed machine learning algorithms at scale — Deeplearning, Random forests, GBM, KMeans, PCA, GLM
  • Clever Algorithms For Machine Learning
  • Machine Learning For Hackers
  • nnet – nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
  • rpart – rpart: Recursive Partitioning and Regression Trees
  • randomForest – randomForest: Breiman and Cutler’s random forests for classification and regression
  • lasso2 – lasso2: L1 constrained estimation aka ‘lasso’
  • gbm – gbm: Generalized Boosted Regression Models
  • e1071 – e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
  • tgp – tgp: Bayesian treed Gaussian process models
  • rgp – rgp: R genetic programming framework
  • arules – arules: Mining Association Rules and Frequent Itemsets
  • frbs – frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
  • rattle – rattle: Graphical user interface for data mining in R
  • ahaz – ahaz: Regularization for semiparametric additive hazards regression
  • arules – arules: Mining Association Rules and Frequent Itemsets
  • bigrf – bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets
  • bigRR – bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)
  • bmrm – bmrm: Bundle Methods for Regularized Risk Minimization Package
  • Boruta – Boruta: A wrapper algorithm for all-relevant feature selection
  • bst – bst: Gradient Boosting
  • C50 – C50: C5.0 Decision Trees and Rule-Based Models
  • caret – caret: Classification and Regression Training
  • CORElearn – CORElearn: Classification, regression, feature evaluation and ordinal evaluation
  • CoxBoost – CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
  • Cubist – Cubist: Rule- and Instance-Based Regression Modeling
  • earth – earth: Multivariate Adaptive Regression Spline Models
  • elasticnet – elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA
  • ElemStatLearn – 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
  • evtree – evtree: Evolutionary Learning of Globally Optimal Trees
  • frbs – frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
  • GAMBoost – GAMBoost: Generalized linear and additive models by likelihood based boosting
  • gamboostLSS – gamboostLSS: Boosting Methods for GAMLSS
  • glmnet – glmnet: Lasso and elastic-net regularized generalized linear models
  • glmpath – glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
  • GMMBoost – GMMBoost: Likelihood-based Boosting for Generalized mixed models
  • grplasso – grplasso: Fitting user specified models with Group Lasso penalty
  • grpreg – grpreg: Regularization paths for regression models with grouped covariates
  • hda – hda: Heteroscedastic Discriminant Analysis
  • ipred – ipred: Improved Predictors
  • kernlab – kernlab: Kernel-based Machine Learning Lab
  • klaR – klaR: Classification and visualization
  • lars – lars: Least Angle Regression, Lasso and Forward Stagewise
  • LiblineaR – LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library
  • LogicReg – LogicReg: Logic Regression
  • maptree – maptree: Mapping, pruning, and graphing tree models
  • mboost – mboost: Model-Based Boosting
  • mvpart – mvpart: Multivariate partitioning
  • ncvreg – ncvreg: Regularization paths for SCAD- and MCP-penalized regression models
  • nnet – nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
  • oblique.tree – oblique.tree: Oblique Trees for Classification Data
  • pamr – pamr: Pam: prediction analysis for microarrays
  • party – party: A Laboratory for Recursive Partytioning
  • partykit – partykit: A Toolkit for Recursive Partytioning
  • penalized – penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
  • penalizedLDA – penalizedLDA: Penalized classification using Fisher’s linear discriminant
  • penalizedSVM – penalizedSVM: Feature Selection SVM using penalty functions
  • quantregForest – quantregForest: Quantile Regression Forests
  • randomForest – randomForest: Breiman and Cutler’s random forests for classification and regression
  • randomForestSRC – randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC)
  • rattle – rattle: Graphical user interface for data mining in R
  • rda – rda: Shrunken Centroids Regularized Discriminant Analysis
  • rdetools – rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces
  • REEMtree – REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data
  • relaxo – relaxo: Relaxed Lasso
  • rgenoud – rgenoud: R version of GENetic Optimization Using Derivatives
  • rgp – rgp: R genetic programming framework
  • Rmalschains – Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
  • rminer – rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
  • ROCR – ROCR: Visualizing the performance of scoring classifiers
  • RoughSets – RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
  • rpart – rpart: Recursive Partitioning and Regression Trees
  • RPMM – RPMM: Recursively Partitioned Mixture Model
  • RSNNS – RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
  • RWeka – RWeka: R/Weka interface
  • RXshrink – RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
  • sda – sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection
  • SDDA – SDDA: Stepwise Diagonal Discriminant Analysis
  • svmpath – svmpath: svmpath: the SVM Path algorithm
  • tgp – tgp: Bayesian treed Gaussian process models
  • tree – tree: Classification and regression trees
  • varSelRF – varSelRF: Variable selection using random forests
  • caret – Unified interface to ~150 ML algorithms in R.
  • SuperLearner and subsemble – Multi-algorithm ensemble learning packages.
  • Introduction to Statistical Learning
  • fpc – fpc: Flexible procedures for clustering

Data Analysis / Data Visualization

Scala

Natural Language Processing

  • ScalaNLP – ScalaNLP is a suite of machine learning and numerical computing libraries.
  • Breeze – Breeze is a numerical processing library for Scala.
  • Chalk – Chalk is a natural language processing library.
  • FACTORIE – FACTORIE is a toolkit for deployable probabilistic modeling, 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.

Data Analysis / Data Visualization

  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • Scalding – A Scala API for Cascading
  • Summing Bird – Streaming MapReduce with Scalding and Storm
  • Algebird – Abstract Algebra for Scala
  • xerial – Data management utilities for Scala
  • simmer – Reduce your data. A unix filter for algebird-powered aggregation.
  • PredictionIO – PredictionIO, a machine learning server for software developers and data engineers.
  • BIDMat – CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.

General-Purpose Machine Learning

  • Conjecture – Scalable Machine Learning in Scalding
  • brushfire – decision trees for scalding
  • ganitha – scalding powered machine learning
  • adam – A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
  • bioscala – Bioinformatics for the Scala programming language
  • BIDMach – CPU and GPU-accelerated Machine Learning Library.
  • Figaro – a Scala library for constructing probabilistic models.
  • h2o-sparkling – H2O and Spark interoperability.

Swift

General-Purpose Machine Learning

  • swix – A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development.

Credits

  • Some of the python libraries were cut-and-pasted from vinta
  • The few go reference I found where pulled from this page