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Source channel @githubtrending · Post #14751 · May 25
#c_lang#c#drivers#gpl#hacktoberfest#kernel#operating_system#os#osdev#reactos#win32#win32api#windows#x86 ReactOS is a free, open-source operating system designed to be compatible with Windows applications and drivers, especially those for Windows Server 2003 and later versions. The latest version, 0.4.15, brings major improvements like better USB and driver support, enhanced system stability, 64-bit fixes, and new features in system tools such as Notepad and Paint. It can be tested safely on virtual machines and is ideal for users seeking a Windows-like experience without Microsoft’s software. ReactOS is still in alpha, so it’s best for testing, but it offers a promising alternative for Windows users wanting a free, open-source OS[1][2][3]. https://github.com/reactos/reactos
Search: #automl
@repo_science · Post #3807 · 12/19/2023, 05:08 AM
#AutoML 🐍 AutoML: Build Production-Ready Models Quickly! Learn the basics of building production-ready automated machine learning (AutoML) models. ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----
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@githubtrending · Post #14863 · 06/24/2025, 01:30 PM
#other#automl#chatgpt#data_analysis#data_science#data_visualization#data_visualizations#deep_learning#gpt#gpt_3#jax#keras#machine_learning#ml#nlp#python#pytorch#scikit_learn#tensorflow#transformer This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently. https://github.com/ml-tooling/best-of-ml-python