Displaying 1 to 20 from 74 results

TensorFlow - Artificial Intelligence Library from Google

TensorFlow is a library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

Scikit Learn - Machine Learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.

Apache Mahout - Scalable machine learning library

Apache Mahout has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent pattern mining.

HPCC System - Hadoop alternative

HPCC is a proven and battle-tested platform for manipulating, transforming, querying and data warehousing Big Data. It supports two type of configuration. Thor is responsible for consuming vast amounts of data, transforming, linking and indexing that data. It functions as a distributed file system with parallel processing power spread across the nodes. Roxie, the Data Delivery Engine, provides separate high-performance online query processing and data warehouse capabilities.

Vespa - Yahoo's big data serving engine

Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time. Vespa is serving platform for Yahoo.com, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Gemini, Flickr.

TensorFlowSharp - TensorFlow API for .NET languages

This surfaces the C API as a strongly-typed .NET API for use from C# and F#.The API binding is pretty much done, and at this point, I am polishing the API to make it more pleasant to use from C# and F# and resolving some of the kinks and TODO-items that I left while I was doing the work.

LightGBM - A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks

For more details, please refer to Features.Experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.

DMTK - Microsoft Distributed Machine Learning Toolkit

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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.

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

goml - On-line Machine Learning in Go (and so much more)

While models include traditional, batch learning interfaces, goml includes many models which let you learn in an online, reactive manner by passing data to streams held on channels.The library includes comprehensive tests, extensive documentation, and clean, expressive, modular source code. Community contribution is heavily encouraged.

gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

golearn - Machine Learning for Go

GoLearn is a 'batteries included' machine learning library for Go. Simplicity, paired with customisability, is the goal. We are in active development, and would love comments from users out in the wild. Drop us a line on Twitter.See here for installation instructions.

DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture

Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.Pre-built binaries can be found on TaskCluster. You'll need to download native_client.tar.xz and the appropriate Python wheel package.

oryx - Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning

The Oryx open source project provides infrastructure for lambda-architecture applications on top of Spark, Spark Streaming and Kafka. On this, it provides further support for real-time, large scale machine learning, and end-to-end applications of this support for common machine learning use cases, like recommendations, clustering, classification and regression.

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.

Zipline - A Pythonic Algorithmic Trading Library

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

Winds - Winds is an open source & beautiful RSS reader built using React/Redux/Sails/Node and Stream (https://getstream

Open source & beautiful RSS reader built using React/Redux/Sails/Node 7 and Stream (getstream.io). Showcases personalized feeds (using machine learning similar to Facebook, Flipboard, Etsy, and Quora - powered by the getstream.io API).This tutorial explains how the personalization API works (blogpost). Check out the hosted demo at https://winds.getstream.io.

MLIB - Apache Spark's scalable machine learning library

MLlib is a Spark implementation of some common machine learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction and lot more.

PredictionIO - Machine Learning Server

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. It helps to predict user behaviors.