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Showing 3767 open source projects for "tasks"
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StarSpace
Learning embeddings for classification, retrieval and ranking
...The training objective is contrastive: for a given query embedding, positive and negative examples are sampled and the model is optimized to score positive higher than negatives. The library supports a variety of tasks (text classification, nearest-neighbor search, recommendation, entity linking) with simple configuration. It includes efficient batching, negative sampling strategies, and on-the-fly embedding updates. -
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Курс Front-End
Kottans frontend course
This repository consists of materials for the frontend of the course. Here you can find the tasks for the qualifying phase, which are required to get to the main part. In order to get to the main part, candidates must complete the tasks listed in Stage 0 . We encourage you to be active during the learning process and help other students: answer questions in chats, check pool requests, report possible errors and offer solutions. -
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sandmap
Simple CLI with the ability to run pure Nmap engine
...It supports Nmap Scripting Engine workflows, script arguments, Tor routing through proxychains, and multiple scans at the same time. Its module library includes a large collection of scan profiles, which makes it useful for repeatable reconnaissance tasks. It is best suited for security teams, penetration testers, and administrators who need structured network discovery while staying close to the Nmap ecosystem. -
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Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. -
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Microjob
Turn Node.js worker threads into easy-to-use routines
A lightweight Node.js library for running CPU-bound tasks in worker threads, enabling better parallel execution. -
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MetaErg
Metagenome Annotation Pipeline
MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub... -
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webpack.js.org
Repository for webpack documentation and more!
At its core, webpack is a static module bundler for modern JavaScript applications. When webpack processes your application, it internally builds a dependency graph from one or more entry points and then combines every module your project needs into one or more bundles, which are static assets to serve your content from. Now that we've covered much of the backlog of missing documentation, we are starting to heavily review each section of the site's content to sort and structure it... -
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Alien Evolution
A websocket based realtime browser application using the Alien Cipher.
...Alien moves the logic outside of the webtree and provide cgi abilities via the apis that connect the endpoints together and allows Alien to deliver client code on a realtime basis according to user interactions. This approach allows Alien to perform realtime tasks that would be next to impossible to perform with HTTP transfers. Live dev at https://alienzone.host All built on Alien technology. Enjoy =D -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who... -
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Chatito
Dataset generation for AI chatbots, NLP tasks
Chatito is a tool that helps generate datasets for training and validating chatbot models using a simple domain-specific language (DSL). -
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RoboSchool
Open source software for robot simulation, integrated with OpenAI Gym
Roboschool is a set of open source robot simulation environments for reinforcement learning, created as an alternative to the Mujoco physics engine. It integrates with OpenAI Gym and provides a variety of continuous control tasks, including humanoid locomotion, quadrupeds, and robotic arms. The library is built on the Bullet Physics engine, making it accessible without the licensing requirements of Mujoco. Roboschool includes training scripts and examples for applying reinforcement learning algorithms to its environments. While the project has since been deprecated in favor of more modern frameworks, it remains historically significant as a bridge between early reinforcement learning research and scalable, open-access environments. ... -
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Cloudbox
Ansible-based solution for rapidly deploying a Docker media server
Ansible allows for fast deployment of the Cloudbox server stack solution with minimal setup and in as little as 15 minutes. Cloudbox puts "all the pieces together" by automating server tasks, performance tweaks, and application setup, right out of the box. Applications in Docker containers are isolated from each other and allow for quick installs and easy uninstalls. Free and open-source software (FOSS). Collaborate on ideas and improvements. Build and share add-ons on the 'Community' repository. Store media on cloud storage to free up on local storage space. ... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions,... -
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Easy Monitor
Easy Monitor is set of SuperKaramba themes and bash scripts
...Can display infos about your: distro, procesor, memory, disk usage, clock, network speed,.. and even do many: administrative, configuration, installation and backup tasks. -
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An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
Neural MMO is a massively multi-agent simulation environment developed by OpenAI for reinforcement learning research. It provides a persistent, procedurally generated world where thousands of agents can interact, compete, and cooperate in real time. The environment is inspired by Massively Multiplayer Online Role-Playing Games (MMORPGs), featuring resource gathering, combat mechanics, exploration, and survival challenges. Agents learn behaviors in a shared ecosystem that supports long-term... -
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WALKOFF
A flexible, easy to use, automation framework
Faster, smarter, cheaper operations through automation. WALKOFF puts the tools in your hands to easily automate the tedious repetitive tasks dragging your operations down. Act smarter with WALKOFF by automatically gathering data, analyzing data, or visualizing data customized to your requirements. Act faster with WALKOFF by integrating the capabilities you already own to dynamically respond on your terms to your fast-moving environment. Drag and drop workflow editor. -
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Simple MVC Example in Java
A simple example to demonstrate the MVC programming pattern in Java
The MVC pattern is a basic pattern in programming and the most known one. It helps the developer to organize and manage the project by defining clear responsabilities and tasks for every level. During learning, this pattern can help students to better understand the complexity of the application and to reduce this complexity by allowing him to work on one level at a time. This project aims to be an example for the MVC pattern. It is very simple and designed to work by simulating and database. It doesn't need any further configuration. ... -
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TensorFlow Haskell
Haskell bindings for TensorFlow
The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies... -
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nlp_chinese_corpus
Large Scale Chinese Corpus for NLP
...The repository gathers several major datasets, including Chinese Wikipedia entries, news articles, encyclopedia-style question answering data, community question answering data, and Chinese-English translation sentence pairs. Each dataset includes descriptions, download links, structure notes, and examples to help users understand how the data is formatted. The corpora can support tasks such as language model pretraining, word vector training, question answering, title generation, keyword generation, translation, and sentence representation learning. Overall, it is a practical resource hub for building or testing Chinese NLP models with larger and more varied datasets. -
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ELI5
A library for debugging/inspecting machine learning classifiers
...The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. Additionally, the library integrates explanation algorithms such as LIME to interpret predictions from arbitrary machine learning models. -
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YouTubeCrawler
Go-based automation utility that downloads YouTube videos
...The workflow involves specifying one or more URLs (via a simple “url” text file in each folder) and the program uses youtube-dl to fetch video and subtitle, then ffmpeg to overlay the subtitles onto the video track. The architecture follows a command-pattern setup: tasks implement a common interface and are scheduled and executed with concurrency controls (maximum goroutines customizable). It assumes a Linux environment with SSR proxy support, and requires the user to pre-install youtube-dl and ffmpeg. With its focus on automation, the tool is useful for easily archiving multilingual subtitles, prepping content for editing, or creating reference versions of YouTube videos. -
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artext
Probabilistic Noising of Natural Language
Artext is a work on injecting noise into text without affecting the core meaning for a human reader. This kind of data can be useful for many NLP tasks, particulary to make models robust to erroneous text. This is a work in progress, and we will publish the results of our experiments soon. Meanwhile, if you use artext in your research please cite this repository. Github: https://github.com/nlpcl-lab/artext -
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Azure Machine Learning Python SDK
Python notebooks with ML and deep learning examples
Azure Machine Learning Python SDK is a curated repository of Python-based Jupyter notebooks that demonstrate how to develop, train, evaluate, and deploy machine learning and deep learning models using the Azure Machine Learning Python SDK. The content spans a wide range of real-world tasks — from foundational quickstarts that teach users how to configure an Azure ML workspace and connect to compute resources, to advanced tutorials on using pipelines, automated machine learning, and dataset handling. Because it is designed to work with Azure Machine Learning compute instances, many notebooks can be executed directly in the cloud without additional setup, but they can also run locally with the appropriate SDK and packages installed. ... -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. ...
