Displaying 1 to 17 from 17 results

dlib - A toolkit for making real world machine learning and data analysis applications in C++

  •    C++

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference. Doing so will make some things run faster.

mlpack - mlpack: a scalable C++ machine learning library --

  •    C++

mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings. Citations are beneficial for the growth and improvement of mlpack.

thinc - 🔮 spaCy's Machine Learning library for NLP in Python

  •    Assembly

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0. Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

auto_ml - Automated machine learning for analytics & production

  •    Python

auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.




magnitude - A fast, efficient universal vector embedding utility package.

  •    Python

A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. Vector space embedding models have become increasingly common in machine learning and traditionally have been popular for natural language processing applications. A fast, lightweight tool to consume these large vector space embedding models efficiently is lacking.

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

  •    Java

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.

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

  •    C++

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.


JGAAP - The Java Graphical Authorship Attribution Program

  •    Java

JGAAP is a tool to allow nonexperts to use cutting edge machine learning techniques on text attribution problems. JGAAP is developed by the Evaluating Variation in Language (EVL) Lab at Duquesne university.

artos - Adaptive Real-Time Object Detection System with HOG and CNN Features

  •    C++

ARTOS is the Adaptive Real-Time Object Detection System, created at the University of Jena (Germany). It can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. It provides an average of 300-500 images with bounding box annotations for more than 3,000 of those categories and, thus, is suitable for object detection. The purpose of ARTOS is not limited to using those images in combination with clustering and a technique called Whitened Histograms of Orientations (WHO, Hariharan et al.) to quickly learn new models, but also includes adapting those models to other domains using in-situ images and applying them to detect objects in images and video streams.

libmaxdiv - Implementation of the Maximally Divergent Intervals algorithm for Anomaly Detection in multivariate spatio-temporal time-series

  •    C++

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection. Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. An efficient C++ implementation called libmaxdiv is provided in maxdiv/libmaxdiv and may be used stand-alone. If it has been built in maxdiv/libmaxdiv/bin, it will be used automatically by the GUI and the maxdiv function in the maxdiv.maxdiv Python package. See maxdiv/libmaxdiv/README.md for build instructions. Otherwise, the pure Python implementation of the MDI algorithm will be used, which is not recommended, since it is extremely slow and lacks some features such as support for spatial data.

deeplearning4j-docs - Documentation for Deeplearning4j - Deep Learning for the JVM, Java & Scala

  •    HTML

The documentation for Deeplearning4j and all of its libraries (DL4J, ND4J, Arbiter, DataVec, etc.) live in this repository. Warning: DO NOT edit the user guide directly in this repository. Commits will be reverted. Please make changes to the main repository here, run the autogeneration process, and open a pull request.

pyltr - Python learning to rank (LTR) toolkit

  •    Python

pyltr is a Python learning-to-rank toolkit with ranking models, evaluation metrics, data wrangling helpers, and more. This software is licensed under the BSD 3-clause license (see LICENSE.txt).

MLKit - A simple machine learning framework written in Swift 🤖

  •    Swift

MLKit is a simple machine learning framework written in Swift. Currently MLKit features machine learning algorithms that deal with the topic of regression, but the framework will expand over time with topics such as classification, clustering, recommender systems, and deep learning. The vision and goal of this framework is to provide developers with a toolkit to create products that can learn from data. MLKit is a side project of mine in order to make it easier for developers to implement machine learning algorithms on the go, and to familiarlize myself with machine learning concepts. This project is under active development and is not ready for use in commercial or personal projects.

saul - Saul : Declarative Learning-Based Programming

  •    Scala

The flexibility in designing above components helps rapid development of intelligent AI systems with one or more learned functions that interact with each other. Saul offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application. With Saul, the details of feature extraction, learning, model evaluation, and inference are all abstracted away from the programmer, leaving him to reason more directly about his application. The api docs are included here.