Search Results for "computer network projects"
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573 projects for "computer network projects" with 1 filter applied:
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Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. ... -
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CVPR 2026
Collection of CVPR 2026 Papers and Open Source Projects
CVPR2026-Papers-with-Code is a community-maintained repository that collects research papers and corresponding open-source implementations from the CVPR 2026 conference and related computer vision research. The repository acts as a continuously updated catalog of cutting-edge research across a wide range of topics including computer vision, multimodal AI, generative models, diffusion systems, autonomous driving, medical imaging, and remote sensing. Each entry typically links to the research... -
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GeoLite.mmdb
MaxMind's GeoIP2 GeoLite2 Country, City, and ASN databases
¿GeoLite.mmdb is a repository that provides MaxMind GeoLite2 databases in MMDB format for Country, City, and ASN data. It is meant for projects that need IP geolocation data without manually navigating MaxMind downloads every time. The repository offers direct download links for each database type, making it easier to integrate GeoLite2 files into scripts, deployments, or network tools. These databases can support use cases such as traffic analysis, regional routing, analytics enrichment, firewall rules, and location-aware services. ... -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Mirror
#1 Open Source Unity Networking Library
Mirror is a high-level open-source networking library for Unity multiplayer games. It was originally based on UNET and has evolved into a stable, modular framework used by many production Unity projects. The library is designed to make multiplayer development easier by handling common networking patterns such as network identities, synchronization, remote procedure calls, authority, spawning, and transport integration. Mirror supports multiple low-level transports, making it adaptable to different game genres and deployment needs. ... -
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The FreeMoCap Project
Free Motion Capture for Everyone
FreeMoCap is an open-source markerless motion capture system that enables users to record human movement using ordinary cameras and convert the footage into usable 3D motion data. The project’s goal is to democratize motion capture by removing the need for expensive suits or proprietary studio hardware, instead relying on computer vision and pose estimation pipelines. It processes synchronized video feeds to reconstruct skeletal motion, which can then be exported for animation, biomechanics research, or creative projects. FreeMoCap includes tools for calibration, recording, processing, and visualization, allowing users to move from raw footage to structured motion data within a single ecosystem. ... -
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Archbase
Open source version of "Fundamentals of Computer Architecture"
The archbase repository appears to focus on architectural patterns, foundational frameworks, or base scaffolds for building software systems (though exact intent may depend on the content). The name “archbase” suggests that it could be a baseline architecture or reference templates for projects, possibly spanning front-end, back-end, or full stacks. The repo likely provides starter code, directory structures, configuration files, core modules, and guidelines that enforce certain... -
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fvcore
Collection of common code shared among different research projects
...The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. Because it is small, stable, and well-tested, fvcore is frequently imported by projects like Detectron2 and PyTorchVideo to avoid duplicating infrastructure and to keep research repos. -
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Apache Log4j
Apache Log4j 2 is a versatile, feature-rich, efficient logging API
Apache Log4j is a versatile, industrial-grade Java logging framework composed of an API, its implementation, and components to assist the deployment for various use cases. Log4j is used by 8% of the Maven ecosystem and listed as one of the top 100 critical open source software projects. The project is actively maintained by a team of several volunteers and supported by a big community. -
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qxresearch-event-1
Python hands on tutorial with 50+ Python Application
qxresearch-event-1 is an open-source educational repository that provides a collection of lightweight Python applications designed to demonstrate programming concepts and artificial intelligence techniques in simple and accessible examples. The repository contains dozens of small programs, many implemented with minimal lines of code, covering topics such as machine learning, graphical user interfaces, computer vision, and API integration. Each example is designed to illustrate a single... -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. -
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Python Code Tutorials
The Python Code Tutorials
...Many tutorials focus on practical implementations such as building network scanners, web scraping tools, object detection systems, and automation utilities using Python libraries. The repository is organized into thematic directories that group tutorials by topic, allowing learners to navigate easily between areas such as ethical hacking, multimedia processing, or machine learning. -
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Internet Pi
Raspberry Pi config for all things Internet
Internet Pi is an Ansible playbook and configuration framework for a Raspberry Pi (or similar single-board computer) that transforms it into a network-infrastructure monitoring and ad-blocking unit. The typical use case is: plug the Pi into your home network, install Pi-hole for DNS-level ad-blocking and privacy, and deploy Prometheus + Grafana to monitor internet connection quality, ping latency, speed tests, and uptime trends. The intention is both practical (reduce ads, manage DNS) and observational (track your internet link over time, identify drops, latency spikes, or service degradation). ... -
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ai-notebooks
Some ipython notebooks implementing AI algorithms
ai-notebooks is a collection of Jupyter notebooks that implements machine-learning and artificial-intelligence ideas in compact, inspectable experiments. The examples are written primarily in Python and use frameworks including TensorFlow, PyTorch, Keras, JAX, and tinygrad. Projects explore problems such as MNIST learning, GANs, VAEs, model compression, and knowledge distillation. Other notebooks examine reinforcement learning through PPO, SAC, TD3, VPG, and MuZero experiments. Transformer, recurrent-network, counterfactual-regret, and normalizing-flow examples broaden the collection. The notebooks are designed to be read directly on GitHub, making the repository useful for studying algorithms through working code. -
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Build your own X
Master programming by recreating your favorite technologies
build-your-own-x is a massive, community-curated roadmap of hands-on tutorials that teach you to re-implement complex systems from scratch—things like databases, compilers, operating systems, interpreters, web servers, neural networks, regex engines, and more. Rather than offering abstract theory, it organizes step-by-step guides by topic and by programming language, so you can pick a project that fits your stack and skill level. The focus is on demystifying internals: you don’t just use a... -
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HuixiangDou
Overcoming Group Chat Scenarios with LLM-based Technical Assistance
...This design allows the system to participate in group discussions without flooding the chat with unnecessary messages. The assistant uses retrieval and ranking methods along with language model reasoning to produce accurate answers for technical topics such as computer vision and machine learning projects. It can be integrated into messaging platforms such as WeChat or other team collaboration tools to assist developer communities. -
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thorough-pytorch
PyTorch Getting Started Tutorial, read online
...The project encourages collaborative learning and often organizes materials in a step-by-step progression that gradually increases in complexity. Topics include neural network fundamentals, training procedures, model evaluation, and practical deep learning workflows. By combining structured lessons with programming projects, the repository aims to help learners develop both conceptual understanding and practical implementation skills. -
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Map of GitHub
Inspirational Mapping
...The project processes GitHub account metadata and GPS or location information (when available) to plot users’ locations and draw connections between communities, resulting in an exploratory visualization where density and network effects become instantly visible. Users can zoom in to explore hotspots like major metropolitan areas or zoom out to see global patterns of activity, making it both a data analytics tool and a piece of data art. The map highlights where developers live, where projects originate, and how open-source participation varies around the world, offering insights into the geography of technology culture and innovation. -
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Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
Learn PyTorch for Deep Learning is an open-source educational repository that provides the full learning materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course created by Daniel Bourke. The project is designed to teach beginners how to build deep learning models using PyTorch through a hands-on, code-first learning approach. Instead of focusing heavily on theory alone, the repository encourages learners to experiment with code and develop practical machine learning... -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
...The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows. -
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Codebase to Course
A Claude Code skill that turns any codebase into an HTML course
...It is particularly targeted at “vibe coders,” or users who rely on AI tools to build software but want deeper insight into how their projects function. -
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CodeWorld
Educational computer programming environment using Haskell
CodeWorld is an educational programming environment that uses a Haskell-inspired language to teach computational thinking through graphics and interactive animation. The web-based IDE provides immediate visual feedback: students write code that draws shapes, composes pictures, and responds to events to build simple games and simulations. Its API emphasizes mathematics and geometry rather than low-level UI details, making it approachable for classrooms and self-learners. Projects run in the... -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. -
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xgplayer
A HTML5 video player with a parser that saves traffic
...It abstracts many of the lower-level complexities of HTML5 media, providing a consistent API for playback control, custom UI overlays, adaptive streaming, plugin hooks, and cross-browser compatibility. Because of its emphasis on modularity and extensibility, xgplayer can be embedded into modern web projects and customized — developers can add controls, custom buffering strategies, subtitle handling, adaptive bitrate streaming, or integrate with other web-based video infrastructures. It seeks to provide a smooth, stable viewing experience even on varied devices or network conditions, and is particularly appealing for web apps that need more control than vanilla video tags offer. -
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Karpathy-Inspired Claude Code Guidelines
A single CLAUDE.md file to improve Claude Code behavior
Karpathy-Inspired Claude Code Guidelines is a curated learning and experimentation repository inspired by the work and teaching philosophy of Andrej Karpathy, designed to help learners build practical competence in deep learning, neural networks, and AI infrastructure. The project organizes a progressive path through exercises, notebooks, code examples, and practical mini-projects that echo Karpathy’s approach to “learning by doing,” where students build core concepts from first principles...
