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Showing 5668 open source projects for "virtual windows machine"
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
machine-learning-refined is an educational repository designed to help students and practitioners understand machine learning algorithms through intuitive explanations and interactive examples. The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric... -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
Machine learning basics repository is an educational project that provides plain Python implementations of fundamental machine learning algorithms designed to help learners understand how these methods work internally. Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such... -
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
Machine Learning Foundations repository contains the code, notebooks, and teaching materials used in Jon Krohn’s Machine Learning Foundations curriculum. The project focuses on explaining the fundamental mathematical and computational concepts that underpin modern machine learning and artificial intelligence systems. The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. The... -
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The Machine & Deep Learning Compendium
List of references in my private & single document
The Machine & Deep Learning Compendium is an open-source knowledge repository that compiles summaries, references, and learning materials related to machine learning and deep learning. The project functions as a comprehensive compendium that organizes hundreds of topics covering algorithms, frameworks, research areas, and practical machine learning workflows. Originally created as a personal knowledge base, the repository evolved into a public educational resource designed to help learners... -
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Docker-OSX
Run macOS VM in a Docker! Run near native OSX-KVM in Docker
Run Mac OS X in Docker with near-native performance! X11 Forwarding. iMessage security research! iPhone USB working! macOS in a Docker container. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on... -
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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
Machine Learning Zoomcamp is an open-source repository containing the materials for a comprehensive course that teaches machine learning engineering from fundamentals to deployment. The project is designed to guide learners through the complete lifecycle of developing machine learning systems, starting with data preparation and model training and ending with production deployment. Participants learn how to build regression and classification models using Python libraries such as NumPy,... -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis.... -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. The repository... -
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Cosmopolitan
Build-once run-anywhere c library
Cosmopolitan Libc makes C a build-once run-anywhere language, like Java, except it doesn't need an interpreter or virtual machine. Instead, it reconfigures stock GCC and Clang to output a POSIX-approved polyglot format that runs natively on Linux + Mac + Windows + FreeBSD + OpenBSD + NetBSD + BIOS with the best possible performance and the tiniest footprint imaginable. Cosmopolitan can be compiled from source on any Linux distro. GNU make needs to be installed beforehand. ... -
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Machine Learning Notebooks
Machine Learning Notebooks
Machine Learning Notebooks is an open-source collection of machine learning notebooks designed to provide practical, minimal, and reusable implementations of common AI tasks across different domains. The project focuses on delivering concise, well-structured Jupyter notebooks that demonstrate how to build, train, and evaluate models using modern machine learning frameworks such as PyTorch. Each notebook is intentionally lightweight, avoiding unnecessary complexity so that users can easily... -
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Synapse Machine Learning
Simple and distributed Machine Learning
SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit,... -
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VirtualBox
Virtualization software for x86_64 hardware
VirtualBox is a powerful open-source full virtualization software that allows a user to run one or more “guest” operating systems simultaneously inside “virtual machines” on a single physical “host” machine. It supports a wide variety of host platforms (Linux, Windows, macOS, Solaris, etc.) and guest OSes, enabling, for example, running Linux on a Windows PC, running Windows Server on a Linux host, or even legacy OSes in a controlled environment. This flexibility makes VirtualBox ideal for developers, testers, sysadmins, or hobbyists who need different OS environments, want to test software across systems, or need isolation for development, sandboxing, or security experiments. ... -
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supabase-py
Python Client for Supabase. Query Postgres from Flask, Django
Python Client for Supabase. Query Postgres from Flask, Django, FastAPI. Python user authentication, security policies, edge functions, file storage, and realtime data streaming. Good first issue. -
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macOS (Dockur)
macOS inside a Docker container
dockur/macos packages a macOS virtual machine inside a Docker container using QEMU/KVM, making it easy to spin up a macOS environment on a Linux host. It provides sensible defaults for firmware, disk, and devices, plus a browser-accessible VNC console so you can complete installation and interact with the desktop remotely. Typical inputs—like environment variables and mounted volumes—control edition choice, storage persistence, and resource sizing, which makes runs repeatable and disposable. ... -
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Lumen Framework
The Laravel Lumen Framework
...Lumen attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as routing, database abstraction, queueing, and caching. The Lumen framework has a few system requirements. Of course, all of these requirements are satisfied by the Laravel Homestead virtual machine, so it's highly recommended that you use Homestead as your local Lumen development environment. Lumen utilizes Composer to manage its dependencies. So, before using Lumen, make sure you have Composer installed on your machine. Since Lumen is a totally separate framework from Laravel, it does not intentionally offer compatibility with any additional Laravel libraries like Cashier, Passport, Scout, etc. -
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AtomVM
Tiny Erlang VM
Brings Erlang, Elixir and other functional languages to really small systems. AtomVM implements from scratch a minimal Erlang VM that supports a subset of ErlangVM features and that is able to run unmodified BEAM binaries on really small systems like MCUs. There is much more information, including a more complete "Getting Started Guide," extensive documentation, examples, and contact information available on the AtomVM project website. AtomVM aims to be easily portable to new platforms with... -
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AutomatedLab
Framework that lets you deploy complex labs on HyperV and Azure
...Requires Windows Management Framework 5+ (Windows). Requires Intel VT-x or AMD/V capable CPU, a decent amount of RAM, and low-latency high-throughput storage (No spinning disks please, as there are issues related to them). This solution supports setting up virtual machines with Windows 7, 2008 R2, 8 / 8.1 and 2012 / 2012 R2, 10 / 2016, 2019, and SQL Server 2008, 2008R2, 2012, 2014, 2016, 2017, 2019. -
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Candle
GRBL controller application with G-Code visualizer written in Qt
GRBL controller application with G-Code visualizer written in Qt. -
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Urbit
An operating function
Urbit is a personal server stack built from scratch. It has an identity layer (Azimuth), virtual machine (Vere), and operating system (Arvo). A running Urbit "ship" is designed to operate with other ships peer-to-peer. Urbit is a general-purpose, peer-to-peer computer and network. Urbit is for everyone, but sometimes it is easy to get lost in this universe. It solves the hard problems of implementing a peer-to-peer network (including identity, NAT traversal, and exactly-once delivery) in the kernel so app developers can focus on business logic. ... -
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Open Source Routing Machine
Open Source Routing Machine - C++ backend
High-performance routing engine written in C++14 designed to run on OpenStreetMap data. There are several services available via HTTP API, C++ library interface and NodeJs wrapper. Nearest, snaps coordinates to the street network and returns the nearest matches. Route finds the fastest route between coordinates. Table computes the duration or distances of the fastest route between all pairs of supplied coordinates. Match snaps noisy GPS traces to the road network in the most plausible way.... -
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HFS (HTTP File Server)
HFS is a web file server to run on your computer
HFS is a lightweight HTTP file server for Windows that allows users to share files over the web easily, turning their local machine into a file server. -
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emuiibo
Virtual amiibo (amiibo emulation) system for Nintendo Switch
Virtual amiibo (amiibo emulation) system for Nintendo Switch. A virtual amiibo is detected by emuiibo based on two aspects: a amiibo.json and a amiibo.flag file must exist inside the virtual amiibo's folder mentioned above. If (for whatever reason) you would like to disable a virtual amiibo from being recognised by emuiibo, just remove the flag file, and create it again to enable it. While old emuiibo formats are supported and converted to the current format (see above), it is strongly... -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning...
