Showing 4819 open source projects for "learning"

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  • 1
    Numbast

    Numbast

    Build an automated pipeline that converts CUDA APIs into Numba

    ...This approach significantly improves developer productivity by reducing boilerplate code and ensuring consistency between C++ and Python interfaces. Numbast is particularly useful for teams working with custom CUDA libraries or extending existing ones into Python ecosystems for data science and machine learning. It complements tools like Numba, which compile Python code into GPU-executable kernels, by expanding the range of accessible CUDA functionality.
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  • 2
    JAX Toolbox

    JAX Toolbox

    Public CI, Docker images for popular JAX libraries

    JAX Toolbox is a development toolkit designed to streamline and optimize the use of JAX for machine learning and high-performance computing on NVIDIA GPUs. It provides prebuilt Docker images, continuous integration pipelines, and optimized example implementations that help developers quickly set up and run JAX workloads without complex configuration. The project supports popular JAX-based frameworks and models, including architectures used for large-scale pretraining such as GPT and LLaMA variants. ...
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  • 3
    Google Kubernetes Engine (GKE) Samples

    Google Kubernetes Engine (GKE) Samples

    Sample applications for Google Kubernetes Engine (GKE)

    ...It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters. The repository is organized into multiple categories such as AI and machine learning, autoscaling, networking, observability, security, and cost optimization, allowing developers to explore specific use cases and architectural patterns. It includes both simple quickstart examples, like basic “hello world” applications, and more advanced scenarios such as migrating monolithic applications to microservices, implementing service meshes, and configuring custom autoscaling metrics.
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  • 4
    MetaScreener

    MetaScreener

    AI-powered tool for efficient abstract and PDF screening

    ...The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with classification and prioritization of candidate papers. The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening.
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  • 5
    NLP-Knowledge-Graph

    NLP-Knowledge-Graph

    Research and application of technologies such as nl processing

    NLP-Knowledge-Graph is an open educational repository that collects resources, research materials, and tutorials focused on the intersection of natural language processing and knowledge graph technologies. The project aims to help researchers and developers understand how structured knowledge representations can enhance language processing systems. It includes curated materials covering key topics such as knowledge graph construction, entity recognition, relation extraction, graph...
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  • 6
    LLM-Finetuning

    LLM-Finetuning

    LLM Finetuning with peft

    LLM-Finetuning is an open educational repository that provides practical notebooks and tutorials for fine-tuning large language models using modern machine learning frameworks. The project focuses on parameter-efficient fine-tuning methods such as LoRA and QLoRA, which allow large models to be adapted to new tasks without requiring full retraining. Instead of requiring specialized hardware or complex training pipelines, many examples are designed to run in cloud notebook environments such as Google Colab. ...
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  • 7
    Open Deep Research

    Open Deep Research

    An AI-powered research assistant that performs iterative research

    ...The system exposes parameters such as breadth and depth to control how widely and how deeply the agent explores information sources. It is intentionally kept compact, with a codebase under roughly 500 lines, making it highly approachable for experimentation and learning. The architecture demonstrates how modern agent pipelines can continuously gather evidence, extract learnings, and adjust research direction over time.
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  • 8
    Qwen3-VL-Embedding

    Qwen3-VL-Embedding

    Multimodal embedding and reranking models built on Qwen3-VL

    Qwen3-VL-Embedding (with its companion Qwen3-VL-Reranker) is a state-of-the-art multimodal embedding and reranking model suite built on the open-sourced Qwen3-VL foundation, developed to handle diverse inputs including text, images, screenshots, and videos. The core embedding model maps such inputs into semantically rich vectors in a unified representation space, enabling similarity search, clustering, and cross-modal retrieval. The reranking model then precisely scores relevance between a...
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  • 9
    Go Katas

    Go Katas

    A collection of daily coding challenges

    Go Katas is a curated collection of practice exercises and coding challenges specifically crafted to improve proficiency in Go, including idiomatic patterns, language fundamentals, and algorithm design. It mirrors the kata practice tradition from martial arts—repetitive, thoughtful practice where each exercise reinforces technique, discipline, and problem-solving approach. Each kata prompt focuses on a precise aspect of Go, such as concurrency patterns, memory management, interfaces, error...
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  • 10
    Z80-μLM

    Z80-μLM

    Z80-μLM is a 2-bit quantized language model

    ...A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment.
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  • 11
    Flight rules for Git

    Flight rules for Git

    Flight rules for git

    ...Each rule is written in a concise, example-oriented style so users can quickly understand what to do and why it matters when they encounter a specific situation. The repository serves as both a learning tool and a quick lookup for urgent scenarios, making Git less intimidating and more predictable.
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  • 12
    MiniMind-V

    MiniMind-V

    "Big Model" trains a visual multimodal VLM with 26M parameters

    ...MiniMind-V combines techniques from modern vision-language modeling but focuses on efficiency and simplicity so that individuals or small teams can explore multimodal learning without massive GPU clusters. It includes training scripts, model definitions, and associated tooling that illustrate how to build and evaluate such lightweight models. While not intended to compete with large production models, it serves as a hands-on educational resource and starting point for experimentation.
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  • 13
    Datumaro

    Datumaro

    Dataset Management Framework, a Python library and a CLI tool to build

    ...Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion.
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  • 14
    Rust Latam

    Rust Latam

    Learn to write Rust procedural macros

    ...The repo contains multiple toy/realistic macro projects drawn from real use-cases: e.g., derive(Builder), derive(CustomDebug), seq!, #[sorted], #[bitfield]. The README indicates the focus is on learning: parsing token streams, generating code, handling generics, attribute arguments, etc. It has test harness and workflow guidance. Because procedural macros are quite subtle in Rust, this workshop is a strong resource for anyone wanting to go from beginner to intermediate/advanced macro writing.
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  • 15
    Docker Curriculum

    Docker Curriculum

    A comprehensive tutorial on getting started with Docker

    Docker Curriculum is a hands-on tutorial that teaches Docker from first principles to practical deployment through a sequence of guided exercises. It starts with images and containers, then layers on volumes, networks, and multi-container composition so learners grasp how pieces fit together. The material emphasizes doing over reading: you build images, run services, and push to registries as you go, reinforcing concepts with real commands. It also connects Docker to everyday developer...
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  • 16
    whoami.filippo.io

    whoami.filippo.io

    A ssh server that knows who you are. $ ssh whoami.filippo.io

    whoami.filippo.io powers a diagnostic service that reports what your client and connection look like from the other side, making it a handy mirror for network and TLS debugging. It surfaces details such as your IP address, protocol versions, cipher suites, SNI, and other attributes that are otherwise tedious to confirm across layers. The tool emphasizes clarity and minimalism, helping engineers quickly verify configuration changes in browsers, proxies, VPNs, or CLI tools. It is especially...
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  • 17
    ComfyUI Examples

    ComfyUI Examples

    Examples of ComfyUI workflows

    ComfyUI_examples is the companion repository for ComfyUI that collects ready-made example workflows, nodes, and compositions to help users learn the node-based interface for AI image generation. Instead of starting from an empty graph, you can open an example and see how prompts, samplers, models, and image processing steps are wired together. This makes ComfyUI more approachable for people coming from “one text box” generators, because they can reverse-engineer complex pipelines visually....
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  • 18
    Hello SQL

    Hello SQL

    Spanish-language course repository that teaches fundamentals of SQL

    ...Learners progress from setup and connection to hands-on exercises that build confidence with CRUD operations and data modeling. The repository’s structure favors incremental learning, with clear folders, references, and exercises you can run locally. It targets absolute beginners as well as developers from other stacks who want a clean, project-based path into SQL.
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  • 19
    Krypt

    Krypt

    Build a Web 3.0 React Solidity blockchain application

    ...It walks you through setting up MetaMask, configuring a local or test blockchain, and wiring contract methods to a clean, modern UI. The project emphasizes end-to-end learning: from authoring and compiling a smart contract, to deploying it, and then interacting with it via web3 libraries in React. It demonstrates core patterns like sending transactions, reading on-chain state, and providing user feedback for pending and confirmed operations. The codebase is structured to be approachable for newcomers while still exposing real-world concerns such as network switching and account permissions. ...
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  • 20
    llm.c

    llm.c

    LLM training in simple, raw C/CUDA

    ...Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. Rather than delivering a production-grade stack, it serves as a reference and learning scaffold for people who want to “see the metal” behind LLMs.
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  • 21
    Uncertainty Baselines

    Uncertainty Baselines

    High-quality implementations of standard and SOTA methods

    Uncertainty Baselines is a collection of strong, well-documented training pipelines that make it straightforward to evaluate predictive uncertainty in modern machine learning models. Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. ...
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  • 22
    Awesome-MCP-ZH

    Awesome-MCP-ZH

    Claude MCP, MCP Servers, MCP Clients

    Awesome-MCP-ZH is a curated, Chinese-language “awesome list” that maps the Model Context Protocol ecosystem for newcomers and practitioners. It organizes learning resources, how-tos, and explainers alongside living catalogs of MCP servers, clients, and tooling so users can get productive quickly. The curation emphasizes beginner-friendly on-ramps, including clients that bundle runtimes and one-click setups, as well as advanced references for power users. Regular updates and community stars indicate ongoing maintenance and adoption within the Chinese developer community. ...
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  • 23
    AReal

    AReal

    Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible

    AReaL is an open source, fully asynchronous reinforcement learning training system. AReal is designed for large reasoning and agentic models. It works with models that perform reasoning over multiple steps, agents interacting with environments. It is developed by the AReaL Team at Ant Group (inclusionAI) and builds upon the ReaLHF project. Release of training details, datasets, and models for reproducibility.
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  • 24
    CSS Inspiration

    CSS Inspiration

    Curated collection of interesting, creative, or inspiring CSS effects

    CSS-Inspiration is a living gallery of modern CSS effects and components that highlights what’s possible with pure CSS and minimal markup. The collection ranges from micro-interactions and loaders to 3D transforms, complex backgrounds, hover states, and text effects, each presented as a small, self-contained demo. Source code is front and center, encouraging developers to study, copy, and tweak snippets for their own projects. Many examples lean on contemporary features—custom properties,...
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  • 25
    Swift Guide

    Swift Guide

    Swift Featured Projects in brain Mapping

    SwiftGuide is a comprehensive, community-maintained guide to the Swift programming language, designed to serve as both a learning resource and a handy reference. It covers all major language aspects: syntax, control flow, functions, closures, generics, protocols, extensions, memory management, concurrency, and the standard library. Each topic typically includes clear explanations, annotated code snippets, and tips for best practices, helping readers understand both how features work and how to use them idiomatically. ...
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