We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.
fathom - A framework for extracting meaning from web pages
Find meaning in the web.
semantic-extraction scoring ranking clusteringCSrankings - A web app for ranking computer science departments according to their research output in selective venues
This ranking of top computer science schools is designed to identify institutions and faculty actively engaged in research across a number of areas of computer science. Unlike US News and World Report's approach, which is exclusively based on surveys, this ranking is entirely metrics-based. It measures the number of publications by faculty that have appeared at the most selective conferences in each area of computer science. This approach is intended to be difficult to game, since publishing in such conferences is generally difficult: contrast this with other approaches like citation-based metrics, which have been repeatedly shown to be easy to manipulate. That said, incorporating citations in some form is a long-term goal.
computer-science ranking computer-science-rankings faculty metrics dblp conferenceRecommenders - Recommender Systems
Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.
recommender-system machine-learning-algorithms ranking recommendation-algorithmsranking - Learning to Rank in TensorFlow
We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. TF-Ranking was presented at premier conferences in Information Retrieval, SIGIR 2019 and ICTIR 2019! The slides are available here.
machine-learning information-retrieval deep-learning ranking learning-to-rank recommender-systemsDeText - A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
DeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks.
nlp deep-neural-networks ranking classification text-embeddings detext-framework neural-networksgo-web-framework-stars - :star: Web frameworks for Go, most starred on Github
:star: Web frameworks for Go, most starred on Github
web-framework stars rankingallRank - allRank is a framework for training learning-to-rank neural models based on PyTorch.
allRank provides an easy and flexible way to experiment with various LTR neural network models and loss functions. It is easy to add a custom loss, and to configure the model and the training procedure. We hope that allRank will facilitate both research in neural LTR and its industrial applications. To help you get started, we provide a run_example.sh script which generates dummy ranking data in libsvm format and trains a Transformer model on the data using provided example config.json config file. Once you run the script, the dummy data can be found in dummy_data directory and the results of the experiment in test_run directory. To run the example, Docker is required.
machine-learning information-retrieval deep-learning pytorch transformer ranking learning-to-rank ndcg click-modelfast-soft-sort - Fast Differentiable Sorting and Ranking
Differentiable sorting and ranking operations in O(n log n). Run python setup.py install or copy the fast_soft_sort/ folder to your project.
sorting tensorflow pytorch ranking differentiable jaxYCML - A Machine Learning and Optimization framework for Objective-C and Swift (MacOS and iOS)
YCML is an Artificial Intelligence, Machine Learning and Optimization framework written in Objective-C. YCML can be used both in Objective-C as well as in Swift. YCML has been verified to run on MacOS and iOS. Above all, YCML attempts to bring high-quality published algorithms to Swift/Objective-C, using optimized implementations. Referenced papers for the implementation of each algorithm are available at the end of this document.
supervised-learning algorithm-implementations machine-learning multi-objective neural-network regression ranking iosgame-of-tests-laravel - Laravel package for a Game of Tests
This package aims to enable a quick implementation of a Game of Tests in Laravel. Is uses the package swisnl/game-of-tests and gives you a set of commands and basic templates to make your own Game of Tests.This package serves as a way to search through git repositories and find PHP tests. I was inspired by the the Spotify testing game I ran in to.
ranking testing laravel package statisticsnode-trueskill - JavaScript implementation of TrueSkill player ranking
##Background This library implements the TrueSkill(TM) ranking system and is a port of the python library https://github.com/dougz/trueskill.
trueskill ranking skill true coffeescriptglicko2js - Glicko2 javascript implementation
The Glicko-2 rating system is a method for assessing a player's strength in games of skill, such as chess and go. The algorithm is explained by its author, Mark E. Glickman, on http://glicko.net/glicko.html. Each player begins with a rating, a rating deviation (accuracy of the rating) and a volatility (speed of rating evolution). These values will evolve according to the outcomes of matches with other players.
glicko utilities rankingchoix - Inference algorithms for models based on Luce's choice axiom
choix is a Python library that provides inference algorithms for models based on Luce's choice axiom. These probabilistic models can be used to explain and predict outcomes of comparisons between items. You can also find more information on the official documentation. In particular, the API reference contains a good summary of the library's features.
machine-learning rankinggithub-user-rank-extension - Your Github fame is getting closer with every open-source project you've built and promoted, with every new follower starring, using and forking your solution
Install GitHub User Rank extension from Chrome Web Store or Mozilla Add-ons Store. Yet another small content script, based on gitlance.net data, it adds rankings straight to GitHub user profiles so you don't have to go anywhere anymore.
browser-extension github-extension ranking rank rankings developer hr recruiting recruitment gitlance githubhidden-gems - Ranking of Steam games to favor "hidden gems".
This repository contains code to compute a ranking of Steam games, based on a score intended to favor "hidden gems". A "hidden gem" is defined as a high-quality game (hence the "gem") which only got little attention (hence "hidden").
steam gems ranking games-list hidden-gemselo-rank - elo ranking system in node
A simple implementation of the elo-ranking system, in javascript. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
elo rank ranking chesscs-ranking - Context-sensitive ranking in Python with Tensorflow
CS-Rank is a Python package for context-sensitive ranking algorithms. Check out our interactive notebooks to quickly find out what our package can do.
machine-learning neural-networks ranking learning-to-rank object-ranking context-aware deep-learning tensorflow
We have large collection of open source products. Follow the tags from
Tag Cloud >>
Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
Add Projects.
