Displaying 1 to 9 from 9 results

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 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.

pygdf - GPU Data Frame

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.

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

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.

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

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.

bihm - Bidirectional Helmholtz Machines

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.

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

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