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

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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 jitpack.io. 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.

machine-learning machine-learning-library machine-learning-algorithms svm tsne jsat

pygdf - GPU Data Frame

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PyGDF implements the Python interface to access and manipulate the GPU Dataframe of GPU Open Analytics Initialive (GOAI). We aim to provide a simple interface that similar to the Pandas dataframe and hide the details of GPU programming.

gpu gpu-data-frame h2o python-api machine-learning-algorithms mapd anaconda analytics

draw - Reimplementation of DRAW

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Reimplementation of DRAW

theano machine-learning-algorithms

nlp - Selected Machine Learning algorithms for basic natural language processing in Golang

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An implementation of selected machine learning algorithms for basic natural language processing in golang. The initial focus for this project is Latent Semantic Analysis to allow retrieval/searching, clustering and classification of text documents based upon semantic content.Built upon the gonum/gonum matrix library with some inspiration taken from Python's scikit-learn.

natural-language-processing nlp lsa latent-semantic-analysis feature-vector machine-learning machine-learning-algorithms svd singular-value-decomposition tf-idf feature-hash feature-extraction

naivebayes - Naive Bayes Classifier for node.js

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Naive Bayes Classifier for node.js.

bayes naive-bayes-classifier naive-bayes naive-bayes-algorithm naivebayes naive machine-learning machine-learning-algorithms classifier node-ml nodejs javascript-library naive-bayes-classification

EdgeML - This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India

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This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency and energy. One example of a ubiquitous real-world application where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.

edge-devices iot-device sensor edge-machine-learning resource-constrained-ml machine-learning-algorithms microsoft-research machine-learning machine-learning-library

socialpowernba - Social Power in the NBA (Comparing on the court performance with Social Influence in R and Python)

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This project and the data explores the relationship between Social Media, Salary, Influence, Performance and Team Valuation in the NBA.

r nba ggplot2 ipython-notebook jupyter-notebook machine-learning machine-learning-algorithms ml prediction social-network social-media salary influence court-performance pie social kaggle kaggle-dataset social-networks

bihm - Bidirectional Helmholtz Machines

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The basic idea is to create a deep generative model for unsupervised learning by combining a top-down directed model P and a bottom up directed model Q into a joint model P*. We show that we can train P* such that P and Q are useful approximate inference distributions when we want to sample from the model, or when we want to perform inference. We generally observe that BiHMs prefer deep architectures with many layers of latent variables. I.e., our best model for the binarized MNIST dataset has 12 layers with 300,200,100,75,50,35,30,25,20,15,10,10 binary latent units. This model reaches a test set LL of 84.8 nats.

theano machine-learning-algorithms

reweighted-ws - Implementation of the reweighted wake-sleep machine learning algorithm

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This repository contains the implementation of the machine learning method described in http://arxiv.org/abs/1406.2751 .

machine-learning-algorithms theano