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Showing 7 open source projects for "linux-gnome"
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Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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Paper2GUI
Convert AI papers to GUI
...It already supports 40+ AI models, covering AI painting, speech synthesis, video frame complementing, video super-resolution, object detection, and image stylization. , OCR recognition and other fields. Support Windows, Mac, Linux systems. Paper2GUI: 一款面向普通人的 AI 桌面 APP 工具箱，免安装即开即用，已支持 40+AI 模型，内容涵盖 AI 绘画、语音合成、视频补帧、视频超分、目标检测、图片风格化、OCR 识别等领域。支持 Windows、Mac、Linux 系统。 -
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tidytext
Text mining using tidy tools
tidytext brings tidy data principles to text mining by converting text into a tidy data frame format. It provides tools for tokenization, sentiment analysis, n‑gram creation, and term‑document matrices, enabling interoperability with dplyr, ggplot2, and other tidyverse workflows. -
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caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via... -
Windocks - Docker Oracle and SQL Server ContainersWindocks is a leader in cloud native database DevOps, recognized by Gartner as a Cool Vendor, and as an innovator by Bloor research in Test Data Management. Novartis, DriveTime, American Family Insurance, and other enterprises rely on Windocks for on-demand database environments for development, testing, and DevOps. Windocks software is easily downloaded for evaluation on standard Linux and Windows servers, for use on-premises or cloud, and for data delivery of SQL Server, Oracle, PostgreSQL, and MySQL to Docker containers or conventional database instances.
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
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mlr
Machine Learning in R
R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods,...
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