Displaying 1 to 20 from 25 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.

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.

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.

Rasa - Create chatbots and voice assistants

  •    Python

Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.




rasa_core - Rasa Core is now part of the Rasa repo: An open source machine learning framework to automate text-and voice-based conversations

  •    Python

Licensed under the Apache License, Version 2.0. Copyright 2019 Rasa Technologies GmbH. Copy of the license. A list of the Licenses of the dependencies of the project can be found at the bottom of the Libraries Summary.

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.

igel - a delightful machine learning tool that allows you to train, test, and use models without writing code

  •    Python

The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.

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).

mlmodelscope - MLModelScope is an open source, extensible, and customizable platform to facilitate evaluation and measurement of ML models within AI pipelines

  •    Javascript

The current landscape of Machine Learning (ML) and Deep Learning (DL) is rife with non-uniform models, frameworks, and system stacks but lacks standard tools to evaluate and profile models or systems. Due to the absence of such tools, the current practice for evaluating and comparing the benefits of proposed AI innovations (be it hardware or software) on end-to-end AI pipelines is both arduous and error prone --- stifling the adoption of the innovations. MLModelScope is a hardware/software agnostic, extensible and customizable platform for evaluating and profiling ML models across datasets/frameworks/hardware, and within AI application pipelines. MLModelScope lowers the cost and effort for performing model evaluation and profiling, making it easier for others to reproduce, evaluate, and analyze acurracy or performance claims of models and systems.

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.