[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-vaaaaanquish--Awesome-Rust-MachineLearning":3,"tool-vaaaaanquish--Awesome-Rust-MachineLearning":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",143909,2,"2026-04-07T11:33:18",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":73,"owner_website":79,"owner_url":80,"languages":81,"stars":90,"forks":91,"last_commit_at":92,"license":93,"difficulty_score":94,"env_os":95,"env_gpu":96,"env_ram":96,"env_deps":97,"category_tags":106,"github_topics":107,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":116,"updated_at":117,"faqs":118,"releases":119},5124,"vaaaaanquish\u002FAwesome-Rust-MachineLearning","Awesome-Rust-MachineLearning","This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀","Awesome-Rust-MachineLearning 是一个专为 Rust 语言打造的机器学习资源宝库。它系统性地整理了用 Rust 编写的各类机器学习库、算法实现，并涵盖了相关的博客、书籍、论文及技术讨论。\n\n在 Python 主导的机器学习领域，许多开发者渴望利用 Rust 的高性能与内存安全特性来优化生产环境，却往往面临生态分散、优质库难寻的痛点。Awesome-Rust-MachineLearning 正是为了解决这一难题而生，它充当了从 Python 迁移至 Rust 的桥梁，帮助用户快速定位成熟可靠的工具链。\n\n这份清单特别适合正在探索 Rust 在 AI 领域应用的开发者、研究人员以及系统架构师。无论是需要基础的数据处理（如 DataFrame、向量运算），还是进阶的深度学习、自然语言处理、强化学习及自动机器学习（AutoML）方案，这里都提供了详尽的分类索引。\n\n其独特亮点在于不仅收录了活跃维护的主流项目，也包含了一些虽已停止更新但具有参考价值的代码库，并对每个类别中的优秀库进行了特别标注和代码点评。此外，它还覆盖了 Jupyter Notebook 支持、GPU ","Awesome-Rust-MachineLearning 是一个专为 Rust 语言打造的机器学习资源宝库。它系统性地整理了用 Rust 编写的各类机器学习库、算法实现，并涵盖了相关的博客、书籍、论文及技术讨论。\n\n在 Python 主导的机器学习领域，许多开发者渴望利用 Rust 的高性能与内存安全特性来优化生产环境，却往往面临生态分散、优质库难寻的痛点。Awesome-Rust-MachineLearning 正是为了解决这一难题而生，它充当了从 Python 迁移至 Rust 的桥梁，帮助用户快速定位成熟可靠的工具链。\n\n这份清单特别适合正在探索 Rust 在 AI 领域应用的开发者、研究人员以及系统架构师。无论是需要基础的数据处理（如 DataFrame、向量运算），还是进阶的深度学习、自然语言处理、强化学习及自动机器学习（AutoML）方案，这里都提供了详尽的分类索引。\n\n其独特亮点在于不仅收录了活跃维护的主流项目，也包含了一些虽已停止更新但具有参考价值的代码库，并对每个类别中的优秀库进行了特别标注和代码点评。此外，它还覆盖了 Jupyter Notebook 支持、GPU 加速及可视化绘图等配套工具，为用户构建完整的 Rust 机器学习工作流提供了全方位指引，让开发者能更高效地发现并利用 Rust 在机器学习领域的最佳实践。","![arml](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fvaaaaanquish_Awesome-Rust-MachineLearning_readme_e4d1c743a631.png)\n\nThis repository is a list of machine learning libraries written in Rust.\nIt's a compilation of GitHub repositories, blogs, books, movies, discussions, papers.\nThis repository is targeted at people who are thinking of migrating from Python. 🦀🐍\n\nIt is divided into several basic library and algorithm categories.\nAnd it also contains libraries that are no longer maintained and small libraries.\nIt has commented on the helpful parts of the code.\nIt also commented on good libraries within each category.\n\nWe can find a better way to use Rust for Machine Learning.\n\n\n- [Website (en)](https:\u002F\u002Fvaaaaanquish.github.io\u002FAwesome-Rust-MachineLearning)\n- [GitHub (en)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning\u002Fblob\u002Fmain\u002FREADME.md)\n- [GitHub (ja)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning\u002Fblob\u002Fmain\u002FREADME.ja.md)\n\n\n# ToC\n\n- [Support Tools](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#support-tools)\n    - [Jupyter Notebook](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#jupyter-notebook)\n    - [Graph Plot](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graph-plot)\n    - [Vector](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#vector)\n    - [Dataframe](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#dataframe)\n    - [Image Processing](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#image-processing)\n    - [Natural Language Processing (preprocessing)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#natural-language-processing-preprocessing)\n    - [Graphical Modeling](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graphical-modeling)\n    - [Interface & Pipeline & AutoML](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#interface--pipeline--automl)\n    - [Workflow](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#workflow)\n    - [GPU](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#gpu)\n- [Comprehensive (like sklearn)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#comprehensive-like-sklearn)\n- [Comprehensive (statistics)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#comprehensive-statistics)\n- [Gradient Boosting](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#gradient-boosting)\n- [Deep Neural Network](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#deep-neural-network)\n- [Graph Model](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graph-model)\n- [Natural Language Processing (model)](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#natural-language-processing-model)\n- [Recommendation](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#recommendation)\n- [Information Retrieval](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#information-retrieval)\n    - [Full Text Search](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#full-text-search)\n    - [Nearest Neighbor Search](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#nearest-neighbor-search)\n- [Reinforcement Learning](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#reinforcement-learning)\n- [Supervised Learning](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#supervised-learning-model)\n- [Unsupervised Learning & Clustering Model](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#unsupervised-learning--clustering-model)\n- [Statistical Model](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#statistical-model)\n- [Evolutionary Algorithm](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#evolutionary-algorithm)\n- [Reference](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#reference)\n    - [Nearby Projects](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#nearby-projects)\n    - [Blogs](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#blogs)\n        - [Introduction](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#introduction)\n        - [Tutorial](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#tutorial)\n        - [Apply](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#apply)\n        - [Case Study](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#case-study)\n    - [Discussion](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#discussion)\n    - [Books](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#books)\n    - [Movie](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#movie)\n    - [PodCast](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#podcast)\n    - [Paper](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#paper)\n- [Thanks](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#thanks)\n\n\n\n# Support Tools\n\n\n## Jupyter Notebook\n\n`evcxr` can be handled as Jupyter Kernel or REPL. It is helpful for learning and validation.\n\n- [google\u002Fevcxr](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fevcxr) - An evaluation context for Rust.\n- [emakryo\u002Frustdef](https:\u002F\u002Fgithub.com\u002Femakryo\u002Frustdef) - Jupyter extension for rust.\n- [murarth\u002Frusti](https:\u002F\u002Fgithub.com\u002Fmurarth\u002Frusti) - REPL for the Rust programming language\n\n\n## Graph Plot\n\nIt might want to try `plotters` for now.\n\n- [38\u002Fplotters](https:\u002F\u002Fgithub.com\u002F38\u002Fplotters) - A rust drawing library for high quality data plotting for both WASM and native, statically and realtimely 🦀 📈🚀\n- [igiagkiozis\u002Fplotly](https:\u002F\u002Fgithub.com\u002Figiagkiozis\u002Fplotly) - Plotly for Rust\n- [milliams\u002Fplotlib](https:\u002F\u002Fgithub.com\u002Fmilliams\u002Fplotlib) - Data plotting library for Rust\n- [tiby312\u002Fpoloto](https:\u002F\u002Fgithub.com\u002Ftiby312\u002Fpoloto) - A simple 2D plotting library that outputs graphs to SVG that can be styled using CSS.\n- [askanium\u002Frustplotlib](https:\u002F\u002Fgithub.com\u002Faskanium\u002Frustplotlib) - A pure Rust visualization library inspired by D3.js\n- [SiegeLord\u002FRustGnuplot](https:\u002F\u002Fgithub.com\u002FSiegeLord\u002FRustGnuplot) - A Rust library for drawing plots, powered by Gnuplot.\n- [saona-raimundo\u002Fpreexplorer](https:\u002F\u002Fgithub.com\u002Fsaona-raimundo\u002Fpreexplorer) - Externalize easily the plotting process from Rust to gnuplot.\n- [procyon-rs\u002Fvega_lite_4.rs](https:\u002F\u002Fgithub.com\u002Fprocyon-rs\u002Fvega_lite_4.rs) - rust api for vega-lite v4\n    - [procyon-rs\u002Fshowata](https:\u002F\u002Fgithub.com\u002Fprocyon-rs\u002Fshowata) - A library of to show data (in browser, evcxr_jupyter) as table, chart...\n- [coder543\u002Fdataplotlib](https:\u002F\u002Fgithub.com\u002Fcoder543\u002Fdataplotlib) - Scientific plotting library for Rust\n- [shahinrostami\u002Fchord_rs](https:\u002F\u002Fgithub.com\u002Fshahinrostami\u002Fchord_rs) - Rust crate for creating beautiful interactive Chord Diagrams. Pro version available at https:\u002F\u002Fm8.fyi\u002Fchord\n\n\nASCII line graph:\n\n- [loony-bean\u002Ftextplots-rs](https:\u002F\u002Fgithub.com\u002Floony-bean\u002Ftextplots-rs) Terminal plotting library for Rust\n- [orhanbalci\u002Frasciigraph](https:\u002F\u002Fgithub.com\u002Forhanbalci\u002Frasciigraph) Zero dependency Rust crate to make lightweight ASCII line graph ╭┈╯ in command line apps with no other dependencies.\n- [jakobhellermann\u002Fpiechart](https:\u002F\u002Fgithub.com\u002Fjakobhellermann\u002Fpiechart) a rust crate for drawing fancy pie charts in the terminal\n- [milliams\u002Fplot](https:\u002F\u002Fgithub.com\u002Fmilliams\u002Fplot) Command-line plotting tool written in Rust\n\n\nExamples:\n\n- Plotters Developer's Guide - Plotter Developer's Guide [https:\u002F\u002Fplotters-rs.github.io\u002Fbook\u002Fintro\u002Fintroduction.html](https:\u002F\u002Fplotters-rs.github.io\u002Fbook\u002Fintro\u002Fintroduction.html)\n- Plotly.rs - Plotly.rs Book [https:\u002F\u002Figiagkiozis.github.io\u002Fplotly\u002Fcontent\u002Fplotly_rs.html](https:\u002F\u002Figiagkiozis.github.io\u002Fplotly\u002Fcontent\u002Fplotly_rs.html)\n- petgraph_review [https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html](https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html)\n- evcxr-jupyter-integration [https:\u002F\u002Fplotters-rs.github.io\u002Fplotters-doc-data\u002Fevcxr-jupyter-integration.html](https:\u002F\u002Fplotters-rs.github.io\u002Fplotters-doc-data\u002Fevcxr-jupyter-integration.html)\n- Rust for Data Science: Tutorial 1 - DEV Community [https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j](https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j)\n- Preface | Data Crayon [https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F](https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F)\n- Drawing SVG Graphs with Rust [https:\u002F\u002Fcetra3.github.io\u002Fblog\u002Fdrawing-svg-graphs-rust\u002F](Drawing SVG Graphs with Rust https:\u002F\u002Fcetra3.github.io\u002Fblog\u002Fdrawing-svg-graphs-rust\u002F)\n\n\n## Vector\n\nMost things use `ndarray` or `std::vec`. \n\nAlso, look at `nalgebra`. When the size of the matrix is known, it is valid.\nSee also: [ndarray vs nalgebra - reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fbtn1cz\u002Fndarray_vs_nalgebra\u002F)\n\n- [dimforge\u002Fnalgebra](https:\u002F\u002Fgithub.com\u002Fdimforge\u002Fnalgebra) - Linear algebra library for Rust.\n- [rust-ndarray\u002Fndarray](https:\u002F\u002Fgithub.com\u002Frust-ndarray\u002Fndarray) - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations\n- [AtheMathmo\u002Frulinalg](https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frulinalg) - A linear algebra library written in Rust\n- [arrayfire\u002Farrayfire-rust](https:\u002F\u002Fgithub.com\u002Farrayfire\u002Farrayfire-rust) - Rust wrapper for ArrayFire\n- [bluss\u002Farrayvec](https:\u002F\u002Fgithub.com\u002Fbluss\u002Farrayvec) - A vector with a fixed capacity. (Rust)\n- [vbarrielle\u002Fsprs](https:\u002F\u002Fgithub.com\u002Fvbarrielle\u002Fsprs) - sparse linear algebra library for rust\n- [liborty\u002Frstats](https:\u002F\u002Fgithub.com\u002Fliborty\u002Frstats) - Rust Statistics and Vector Algebra Library\n- [PyO3\u002Frust-numpy](https:\u002F\u002Fgithub.com\u002FPyO3\u002Frust-numpy) - PyO3-based Rust binding of NumPy C-API\n\n\n## Dataframe\n\nIt might want to try `polars` for now. `datafusion` looks good too.\n\n- [ritchie46\u002Fpolars](https:\u002F\u002Fgithub.com\u002Fritchie46\u002Fpolars) - Rust DataFrame library\n- [apache\u002Farrow](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow-rs) - In-memory columnar format, in Rust.\n- [apache\u002Farrow-datafusion](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow-datafusion) - Apache Arrow DataFusion and Ballista query engines\n- [milesgranger\u002Fblack-jack](https:\u002F\u002Fgithub.com\u002Fmilesgranger\u002Fblack-jack) - DataFrame \u002F Series data processing in Rust\n- [nevi-me\u002Frust-dataframe](https:\u002F\u002Fgithub.com\u002Fnevi-me\u002Frust-dataframe) - A Rust DataFrame implementation, built on Apache Arrow\n- [kernelmachine\u002Futah](https:\u002F\u002Fgithub.com\u002Fkernelmachine\u002Futah) - Dataframe structure and operations in Rust\n- [sinhrks\u002Fbrassfibre](https:\u002F\u002Fgithub.com\u002Fsinhrks\u002Fbrassfibre) - Provides multiple-dtype columner storage, known as DataFrame in pandas\u002FR\n\n\n## Image Processing\n\nIt might want to try `image-rs` for now. Algorithms such as linear transformations are implemented in other libraries as well.\n\n- [image-rs\u002Fimage](https:\u002F\u002Fgithub.com\u002Fimage-rs\u002Fimage) - Encoding and decoding images in Rust\n    - [image-rs\u002Fimageproc](https:\u002F\u002Fgithub.com\u002Fimage-rs\u002Fimageproc) - Image processing operations\n- [rust-cv\u002Fndarray-image](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fndarray-image) - Allows conversion between ndarray's types and image's types\n- [rust-cv\u002Fcv](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fcv) - Rust CV mono-repo. Contains pure-Rust dependencies which attempt to encapsulate the capability of OpenCV, OpenMVG, and vSLAM frameworks in a cohesive set of APIs.\n- [twistedfall\u002Fopencv-rust](https:\u002F\u002Fgithub.com\u002Ftwistedfall\u002Fopencv-rust) - Rust bindings for OpenCV 3 & 4\n- [rustgd\u002Fcgmath](https:\u002F\u002Fgithub.com\u002Frustgd\u002Fcgmath) - A linear algebra and mathematics library for computer graphics.\n- [atomashpolskiy\u002Frustface](https:\u002F\u002Fgithub.com\u002Fatomashpolskiy\u002Frustface) - Face detection library for the Rust programming language\n\n\n## Natural Language Processing (preprocessing)\n\n- [google-research\u002Fdeduplicate-text-datasets](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fdeduplicate-text-datasets) - This repository contains code to deduplicate language model datasets as descrbed in the paper \"Deduplicating Training Data Makes Language Models Better\" by Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch and Nicholas Carlini. This repository contains both the ExactSubstr deduplication implementation (written in Rust) along with the scripts we used in the paper to perform deduplication and inspect the results (written in Python). In an upcoming update, we will add files to reproduce the NearDup-deduplicated versions of the C4, RealNews, LM1B, and Wiki-40B-en datasets.\n- [pemistahl\u002Flingua-rs](https:\u002F\u002Fgithub.com\u002Fpemistahl\u002Flingua-rs) - 👄 The most accurate natural language detection library in the Rust ecosystem, suitable for long and short text alike\n- [usamec\u002Fcntk-rs](https:\u002F\u002Fgithub.com\u002Fusamec\u002Fcntk-rs) - Wrapper around Microsoft CNTK library\n- [stickeritis\u002Fsticker](https:\u002F\u002Fgithub.com\u002Fstickeritis\u002Fsticker) - A LSTM\u002FTransformer\u002Fdilated convolution sequence labeler\n- [tensordot\u002Fsyntaxdot](https:\u002F\u002Fgithub.com\u002Ftensordot\u002Fsyntaxdot) - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.\n- [christophertrml\u002Frs-natural](https:\u002F\u002Fgithub.com\u002Fchristophertrml\u002Frs-natural) - Natural Language Processing for Rust\n- [bminixhofer\u002Fnnsplit](https:\u002F\u002Fgithub.com\u002Fbminixhofer\u002Fnnsplit) - Semantic text segmentation. For sentence boundary detection, compound splitting and more.\n- [greyblake\u002Fwhatlang-rs](https:\u002F\u002Fgithub.com\u002Fgreyblake\u002Fwhatlang-rs) - Natural language detection library for Rust.\n- [finalfusion\u002Ffinalfrontier](https:\u002F\u002Fgithub.com\u002Ffinalfusion\u002Ffinalfrontier) - Context-sensitive word embeddings with subwords. In Rust.\n- [bminixhofer\u002Fnlprule](https:\u002F\u002Fgithub.com\u002Fbminixhofer\u002Fnlprule) - A fast, low-resource Natural Language Processing and Error Correction library written in Rust.\n- [rth\u002Fvtext](https:\u002F\u002Fgithub.com\u002Frth\u002Fvtext) - Simple NLP in Rust with Python bindings\n- [tamuhey\u002Ftokenizations](https:\u002F\u002Fgithub.com\u002Ftamuhey\u002Ftokenizations) - Robust and Fast tokenizations alignment library for Rust and Python\n- [vgel\u002Ftreebender](https:\u002F\u002Fgithub.com\u002Fvgel\u002Ftreebender) - A HDPSG-inspired symbolic natural language parser written in Rust\n- [reinfer\u002Fblingfire-rs](https:\u002F\u002Fgithub.com\u002Freinfer\u002Fblingfire-rs) - Rust wrapper for the BlingFire tokenization library\n- [CurrySoftware\u002Frust-stemmers](https:\u002F\u002Fgithub.com\u002FCurrySoftware\u002Frust-stemmers) - Common stop words in a variety of languages\n- [cmccomb\u002Frust-stop-words](https:\u002F\u002Fgithub.com\u002Fcmccomb\u002Frust-stop-words) - Common stop words in a variety of languages\n- [Freyskeyd\u002Fnlp](https:\u002F\u002Fgithub.com\u002FFreyskeyd\u002Fnlp) - Rust-nlp is a library to use Natural Language Processing algorithm with RUST\n- [Daniel-Liu-c0deb0t\u002Fuwu](https:\u002F\u002Fgithub.com\u002FDaniel-Liu-c0deb0t\u002Fuwu) - fastest text uwuifier in the west\n\n\n## Graphical Modeling\n\n- [alibaba\u002FGraphScope](https:\u002F\u002Fgithub.com\u002Falibaba\u002FGraphScope) - GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba\n- [petgraph\u002Fpetgraph](https:\u002F\u002Fgithub.com\u002Fpetgraph\u002Fpetgraph) - Graph data structure library for Rust.\n- [rs-graph\u002Frs-graph](https:\u002F\u002Fchiselapp.com\u002Fuser\u002Ffifr\u002Frepository\u002Frs-graph\u002Fdoc\u002Frelease\u002FREADME.md) - rs-graph is a library for graph algorithms and combinatorial optimization\n- [metamolecular\u002Fgamma](https:\u002F\u002Fgithub.com\u002Fmetamolecular\u002Fgamma) - A graph library for Rust.\n- [purpleprotocol\u002Fgraphlib](https:\u002F\u002Fgithub.com\u002Fpurpleprotocol\u002Fgraphlib) - Simple but powerful graph library for Rust\n- [yamafaktory\u002Fhypergraph](https:\u002F\u002Fgithub.com\u002Fyamafaktory\u002Fhypergraph) - Hypergraph is a data structure library to generate directed hypergraphs\n\n## Interface & Pipeline & AutoML\n\n- [modelfoxdotdev\u002Fmodelfox](https:\u002F\u002Fgithub.com\u002Fmodelfoxdotdev\u002Fmodelfox) - Modelfox is an all-in-one automated machine learning framework. https:\u002F\u002Fgithub.com\u002Fmodelfoxdotdev\u002Fmodelfox\n- [datafuselabs\u002Fdatafuse](https:\u002F\u002Fgithub.com\u002Fdatafuselabs\u002Fdatafuse) - A Modern Real-Time Data Processing & Analytics DBMS with Cloud-Native Architecture, written in Rust\n- [mstallmo\u002Ftensorrt-rs](https:\u002F\u002Fgithub.com\u002Fmstallmo\u002Ftensorrt-rs) - Rust library for running TensorRT accelerated deep learning models\n- [pipehappy1\u002Ftensorboard-rs](https:\u002F\u002Fgithub.com\u002Fpipehappy1\u002Ftensorboard-rs) - Write TensorBoard events in Rust.\n- [ehsanmok\u002Ftvm-rust](https:\u002F\u002Fgithub.com\u002Fehsanmok\u002Ftvm-rust) - Rust bindings for TVM runtime\n- [vertexclique\u002Forkhon](https:\u002F\u002Fgithub.com\u002Fvertexclique\u002Forkhon) - Orkhon: ML Inference Framework and Server Runtime\n- [xaynetwork\u002Fxaynet](https:\u002F\u002Fgithub.com\u002Fxaynetwork\u002Fxaynet) - Xaynet represents an agnostic Federated Machine Learning framework to build privacy-preserving AI applications\n- [webonnx\u002Fwonnx](https:\u002F\u002Fgithub.com\u002Fwebonnx\u002Fwonnx) - A GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web\n- [sonos\u002Ftract](https:\u002F\u002Fgithub.com\u002Fsonos\u002Ftract) - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference\n- [MegEngine\u002FMegFlow](https:\u002F\u002Fgithub.com\u002FMegEngine\u002FMegFlow) - Efficient ML solutions for long-tailed demands.\n\n\n## Workflow\n\n- [substantic\u002Frain](https:\u002F\u002Fgithub.com\u002Fsubstantic\u002Frain) - Framework for large distributed pipelines\n- [timberio\u002Fvector](https:\u002F\u002Fgithub.com\u002Ftimberio\u002Fvector) - A high-performance, highly reliable, observability data pipeline\n\n\n## GPU\n\n- [Rust-GPU\u002FRust-CUDA](https:\u002F\u002Fgithub.com\u002FRust-GPU\u002FRust-CUDA) - Ecosystem of libraries and tools for writing and executing extremely fast GPU code fully in Rust.\n- [EmbarkStudios\u002Frust-gpu](https:\u002F\u002Fgithub.com\u002FEmbarkStudios\u002Frust-gpu) - 🐉 Making Rust a first-class language and ecosystem for GPU code 🚧\n- [termoshtt\u002Faccel](https:\u002F\u002Fgithub.com\u002Ftermoshtt\u002Faccel) - GPGPU Framework for Rust\n- [kmcallister\u002Fglassful](https:\u002F\u002Fgithub.com\u002Fkmcallister\u002Fglassful) - Rust-like syntax for OpenGL Shading Language\n- [MaikKlein\u002Frlsl](https:\u002F\u002Fgithub.com\u002FMaikKlein\u002Frlsl) - Rust to SPIR-V compiler\n- [japaric-archived\u002Fnvptx](https:\u002F\u002Fgithub.com\u002Fjaparic-archived\u002Fnvptx) - How to: Run Rust code on your NVIDIA GPU\n- [msiglreith\u002Finspirv-rust](https:\u002F\u002Fgithub.com\u002Fmsiglreith\u002Finspirv-rust) - Rust (MIR) → SPIR-V (Shader) compiler\n\n\n\n# Comprehensive (like sklearn)\n\nAll libraries support the following algorithms.\n\n- Linear Regression\n- Logistic Regression\n- K-Means Clustering\n- Neural Networks\n- Gaussian Process Regression\n- Support Vector Machines\n- kGaussian Mixture Models\n- Naive Bayes Classifiers\n- DBSCAN\n- k-Nearest Neighbor Classifiers\n- Principal Component Analysis\n- Decision Tree\n- Support Vector Machines\n- Naive Bayes\n- Elastic Net\n\n\nIt might want to try `smartcore` or `linfa` for now.\n\n- [smartcorelib\u002Fsmartcore](https:\u002F\u002Fgithub.com\u002Fsmartcorelib\u002Fsmartcore) - SmartCore is a comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.\n    - LASSO, Ridge, Random Forest, LU, QR, SVD, EVD, and more metrics\n    - https:\u002F\u002Fsmartcorelib.org\u002Fuser_guide\u002Fquick_start.html\n- [rust-ml\u002Flinfa](https:\u002F\u002Fgithub.com\u002Frust-ml\u002Flinfa) - A Rust machine learning framework.\n    - Gaussian Mixture Model Clustering, Agglomerative Hierarchical Clustering, ICA\n    - https:\u002F\u002Fgithub.com\u002Frust-ml\u002Flinfa#current-state\n- [maciejkula\u002Frustlearn](https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Frustlearn) - Machine learning crate for Rust\n    - factorization machines, k-fold cross-validation, ndcg\n    - https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Frustlearn#features\n- [AtheMathmo\u002Frusty-machine](https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frusty-machine) - Machine Learning library for Rust\n    - Confusion Matrix, Cross Varidation, Accuracy, F1 Score, MSE\n    - https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frusty-machine#machine-learning\n- [benjarison\u002Feval-metrics](https:\u002F\u002Fgithub.com\u002Fbenjarison\u002Feval-metrics) - Evaluation metrics for machine learning\n    - Many evaluation functions\n- [blue-yonder\u002Fvikos](https:\u002F\u002Fgithub.com\u002Fblue-yonder\u002Fvikos) - A machine learning library for supervised training of parametrized models\n- [mbillingr\u002Fopenml-rust](https:\u002F\u002Fgithub.com\u002Fmbillingr\u002Fopenml-rust) - A rust interface to http:\u002F\u002Fopenml.org\u002F\n\n\n# Comprehensive (Statistics)\n\n- [statrs-dev\u002Fstatrs](https:\u002F\u002Fgithub.com\u002Fstatrs-dev\u002Fstatrs) - Statistical computation library for Rust\n- [rust-ndarray\u002Fndarray-stats](https:\u002F\u002Fgithub.com\u002Frust-ndarray\u002Fndarray-stats) - Statistical routines for ndarray\n- [Axect\u002FPeroxide](https:\u002F\u002Fgithub.com\u002FAxect\u002FPeroxide) - Rust numeric library with R, MATLAB & Python syntax\n    - Linear Algebra, Functional Programming, Automatic Differentiation, Numerical Analysis, Statistics, Special functions, Plotting, Dataframe\n- [tarcieri\u002Fmicromath](https:\u002F\u002Fgithub.com\u002Ftarcieri\u002Fmicromath) - Embedded Rust arithmetic, 2D\u002F3D vector, and statistics library\n\n\n# Gradient Boosting\n\n- [mesalock-linux\u002Fgbdt-rs](https:\u002F\u002Fgithub.com\u002Fmesalock-linux\u002Fgbdt-rs) - MesaTEE GBDT-RS : a fast and secure GBDT library, supporting TEEs such as Intel SGX and ARM TrustZone\n- [davechallis\u002Frust-xgboost](https:\u002F\u002Fgithub.com\u002Fdavechallis\u002Frust-xgboost) - Rust bindings for XGBoost.\n- [vaaaaanquish\u002Flightgbm-rs](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Flightgbm-rs) - LightGBM Rust binding\n- [catboost\u002Fcatboost](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost\u002Ftree\u002Fmaster\u002Fcatboost\u002Frust-package) - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks (predict only)\n- [Entscheider\u002Fstamm](https:\u002F\u002Fgithub.com\u002Fentscheider\u002Fstamm) - Generic decision trees for rust\n\n\n# Deep Neural Network\n\n`Tensorflow bindings` and `PyTorch bindings` are the most common.\n`tch-rs` also has torch vision, which is useful.\n\n- [tensorflow\u002Frust](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Frust) - Rust language bindings for TensorFlow\n- [LaurentMazare\u002Ftch-rs](https:\u002F\u002Fgithub.com\u002FLaurentMazare\u002Ftch-rs) - Rust bindings for the C++ api of PyTorch.\n- [VasanthakumarV\u002Feinops](https:\u002F\u002Fgithub.com\u002Fvasanthakumarv\u002Feinops) - Simplistic API for deep learning tensor operations\n- [spearow\u002Fjuice](https:\u002F\u002Fgithub.com\u002Fspearow\u002Fjuice) - The Hacker's Machine Learning Engine\n- [neuronika\u002Fneuronika](https:\u002F\u002Fgithub.com\u002Fneuronika\u002Fneuronika) - Tensors and dynamic neural networks in pure Rust.\n- [bilal2vec\u002FL2](https:\u002F\u002Fgithub.com\u002Fbilal2vec\u002FL2) - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust\n- [raskr\u002Frust-autograd](https:\u002F\u002Fgithub.com\u002Fraskr\u002Frust-autograd) - Tensors and differentiable operations (like TensorFlow) in Rust\n- [charles-r-earp\u002Fautograph](https:\u002F\u002Fgithub.com\u002Fcharles-r-earp\u002Fautograph) - Machine Learning Library for Rust\n- [patricksongzy\u002Fcorgi](https:\u002F\u002Fgithub.com\u002Fpatricksongzy\u002Fcorgi) - A neural network, and tensor dynamic automatic differentiation implementation for Rust.\n- [JonathanWoollett-Light\u002Fcogent](https:\u002F\u002Fgithub.com\u002FJonathanWoollett-Light\u002Fcogent) - Simple neural network library for classification written in Rust.\n- [oliverfunk\u002Fdarknet-rs](https:\u002F\u002Fgithub.com\u002Foliverfunk\u002Fdarknet-rs) - Rust bindings for darknet\n- [jakelee8\u002Fmxnet-rs](https:\u002F\u002Fgithub.com\u002Fjakelee8\u002Fmxnet-rs) - mxnet for Rust\n- [jramapuram\u002Fhal](https:\u002F\u002Fgithub.com\u002Fjramapuram\u002Fhal) - Rust based Cross-GPU Machine Learning\n- [primitiv\u002Fprimitiv-rust](https:\u002F\u002Fgithub.com\u002Fprimitiv\u002Fprimitiv-rust) - Rust binding of primitiv\n- [chantera\u002Fdynet-rs](https:\u002F\u002Fgithub.com\u002Fchantera\u002Fdynet-rs) - The Rust Language Bindings for DyNet\n- [millardjn\u002Falumina](https:\u002F\u002Fgithub.com\u002Fmillardjn\u002Falumina) - A deep learning library for rust\n- [jramapuram\u002Fhal](https:\u002F\u002Fgithub.com\u002Fjramapuram\u002Fhal) - Rust based Cross-GPU Machine Learning\n- [afck\u002Ffann-rs](https:\u002F\u002Fgithub.com\u002Fafck\u002Ffann-rs) - Rust wrapper for the Fast Artificial Neural Network library\n- [autumnai\u002Fleaf](https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fleaf) - Open Machine Intelligence Framework for Hackers. (GPU\u002FCPU)\n- [c0dearm\u002Fmushin](https:\u002F\u002Fgithub.com\u002Fc0dearm\u002Fmushin) - Compile-time creation of neural networks\n- [tedsta\u002Fdeeplearn-rs](https:\u002F\u002Fgithub.com\u002Ftedsta\u002Fdeeplearn-rs) - Neural networks in Rust\n- [sakex\u002Fneat-gru-rust](https:\u002F\u002Fgithub.com\u002Fsakex\u002Fneat-gru-rust) - neat-gru\n- [nerosnm\u002Fn2](https:\u002F\u002Fgithub.com\u002Fnerosnm\u002Fn2) - (Work-in-progress) library implementation of a feedforward, backpropagation artificial neural network\n- [Wuelle\u002Fdeep_thought](https:\u002F\u002Fgithub.com\u002FWuelle\u002Fdeep_thought) - Neural Networks in Rust\n- [MikhailKravets\u002FNeuroFlow](https:\u002F\u002Fgithub.com\u002FMikhailKravets\u002FNeuroFlow) - Awesome deep learning crate\n- [dvigneshwer\u002Fdeeprust](https:\u002F\u002Fgithub.com\u002Fdvigneshwer\u002Fdeeprust) - Machine learning crate in Rust\n- [millardjn\u002Frusty_sr](https:\u002F\u002Fgithub.com\u002Fmillardjn\u002Frusty_sr) - Deep learning superresolution in pure rust\n- [coreylowman\u002Fdfdx](https:\u002F\u002Fgithub.com\u002Fcoreylowman\u002Fdfdx) - Strongly typed Deep Learning in Rust\n\n# Graph Model\n\n- [Synerise\u002Fcleora](https:\u002F\u002Fgithub.com\u002FSynerise\u002Fcleora) - Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.\n- [Pardoxa\u002Fnet_ensembles](https:\u002F\u002Fgithub.com\u002FPardoxa\u002Fnet_ensembles) - Rust library for random graph ensembles\n\n\n# Natural Language Processing (model)\n\n- [huggingface\u002Ftokenizers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftokenizers\u002Ftree\u002Fmaster\u002Ftokenizers) - The core of tokenizers, written in Rust. Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.\n- [guillaume-be\u002Frust-tokenizers](https:\u002F\u002Fgithub.com\u002Fguillaume-be\u002Frust-tokenizers) - Rust-tokenizer offers high-performance tokenizers for modern language models, including WordPiece, Byte-Pair Encoding (BPE) and Unigram (SentencePiece) models\n- [guillaume-be\u002Frust-bert](https:\u002F\u002Fgithub.com\u002Fguillaume-be\u002Frust-bert) - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)\n- [sno2\u002Fbertml](https:\u002F\u002Fgithub.com\u002Fsno2\u002Fbertml) - Use common pre-trained ML models in Deno!\n- [cpcdoy\u002Frust-sbert](https:\u002F\u002Fgithub.com\u002Fcpcdoy\u002Frust-sbert) - Rust port of sentence-transformers (https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers)\n- [vongaisberg\u002Fgpt3_macro](https:\u002F\u002Fgithub.com\u002Fvongaisberg\u002Fgpt3_macro) - Rust macro that uses GPT3 codex to generate code at compiletime\n- [proycon\u002Fdeepfrog](https:\u002F\u002Fgithub.com\u002Fproycon\u002Fdeepfrog) - An NLP-suite powered by deep learning\n- [ferristseng\u002Frust-tfidf](https:\u002F\u002Fgithub.com\u002Fferristseng\u002Frust-tfidf) - Library to calculate TF-IDF\n- [messense\u002Ffasttext-rs](https:\u002F\u002Fgithub.com\u002Fmessense\u002Ffasttext-rs) - fastText Rust binding\n- [mklf\u002Fword2vec-rs](https:\u002F\u002Fgithub.com\u002Fmklf\u002Fword2vec-rs) - pure rust implementation of word2vec\n- [DimaKudosh\u002Fword2vec](https:\u002F\u002Fgithub.com\u002FDimaKudosh\u002Fword2vec) - Rust interface to word2vec.\n- [lloydmeta\u002Fsloword2vec-rs](https:\u002F\u002Fgithub.com\u002Flloydmeta\u002Fsloword2vec-rs) - A naive (read: slow) implementation of Word2Vec. Uses BLAS behind the scenes for speed.\n\n\n# Recommendation\n\n- [PersiaML\u002FPERSIA](https:\u002F\u002Fgithub.com\u002FPersiaML\u002FPERSIA) - High performance distributed framework for training deep learning recommendation models based on PyTorch.\n- [jackgerrits\u002Fvowpalwabbit-rs](https:\u002F\u002Fgithub.com\u002Fjackgerrits\u002Fvowpalwabbit-rs) - 🦀🐇 Rusty VowpalWabbit\n- [outbrain\u002Ffwumious_wabbit](https:\u002F\u002Fgithub.com\u002Foutbrain\u002Ffwumious_wabbit) - Fwumious Wabbit, fast on-line machine learning toolkit written in Rust\n- [hja22\u002Frucommender](https:\u002F\u002Fgithub.com\u002Fhja22\u002Frucommender) - Rust implementation of user-based collaborative filtering\n- [maciejkula\u002Fsbr-rs](https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Fsbr-rs) - Deep recommender systems for Rust\n- [chrisvittal\u002Fquackin](https:\u002F\u002Fgithub.com\u002Fchrisvittal\u002Fquackin) - A recommender systems framework for Rust\n- [snd\u002Fonmf](https:\u002F\u002Fgithub.com\u002Fsnd\u002Fonmf) - fast rust implementation of online nonnegative matrix factorization as laid out in the paper \"detect and track latent factors with online nonnegative matrix factorization\"\n- [rhysnewell\u002Fnymph](https:\u002F\u002Fgithub.com\u002Frhysnewell\u002Fnymph) - Non-Negative Matrix Factorization in Rust\n\n\n# Information Retrieval\n\n## Full Text Search\n\n- [quickwit-inc\u002Fquickwit](https:\u002F\u002Fgithub.com\u002Fquickwit-inc\u002Fquickwit) - Quickwit is a big data search engine.\n- [bayard-search\u002Fbayard](https:\u002F\u002Fgithub.com\u002Fbayard-search\u002Fbayard) - A full-text search and indexing server written in Rust.\n- [neuml\u002Ftxtai.rs](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai.rs) - AI-powered search engine for Rust\n- [meilisearch\u002FMeiliSearch](https:\u002F\u002Fgithub.com\u002Fmeilisearch\u002FMeiliSearch) - Lightning Fast, Ultra Relevant, and Typo-Tolerant Search Engine\n- [toshi-search\u002FToshi](https:\u002F\u002Fgithub.com\u002Ftoshi-search\u002FToshi) - A full-text search engine in rust\n- [BurntSushi\u002Ffst](https:\u002F\u002Fgithub.com\u002FBurntSushi\u002Ffst) - Represent large sets and maps compactly with finite state transducers.\n- [tantivy-search\u002Ftantivy](https:\u002F\u002Fgithub.com\u002Ftantivy-search\u002Ftantivy) - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust\n- [tinysearch\u002Ftinysearch](https:\u002F\u002Fgithub.com\u002Ftinysearch\u002Ftinysearch) - 🔍 Tiny, full-text search engine for static websites built with Rust and Wasm\n- [quantleaf\u002Fprobly-search](https:\u002F\u002Fgithub.com\u002Fquantleaf\u002Fprobly-search) - A lightweight full-text search library that provides full control over the scoring calculations\n- [https:\u002F\u002Fgithub.com\u002Fandylokandy\u002Fsimsearch-rs](https:\u002F\u002Fgithub.com\u002Fandylokandy\u002Fsimsearch-rs) - A simple and lightweight fuzzy search engine that works in memory, searching for similar strings\n- [jameslittle230\u002Fstork](https:\u002F\u002Fgithub.com\u002Fjameslittle230\u002Fstork) - 🔎 Impossibly fast web search, made for static sites.\n- [elastic\u002Felasticsearch-rs](https:\u002F\u002Fgithub.com\u002Felastic\u002Felasticsearch-rs) - Official Elasticsearch Rust Client\n\n\n## Nearest Neighbor Search\n\n- [Enet4\u002Ffaiss-rs](https:\u002F\u002Fgithub.com\u002FEnet4\u002Ffaiss-rs) - Rust language bindings for Faiss\n- [rust-cv\u002Fhnsw](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fhnsw) - HNSW ANN from the paper \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\"\n- [hora-search\u002Fhora](https:\u002F\u002Fgithub.com\u002Fhora-search\u002Fhora) - 🚀 efficient approximate nearest neighbor search algorithm collections library, which implemented with Rust 🦀. horasearch.com\n- [InstantDomain\u002Finstant-distance](https:\u002F\u002Fgithub.com\u002FInstantDomain\u002Finstant-distance) - Fast approximate nearest neighbor searching in Rust, based on HNSW index\n- [lerouxrgd\u002Fngt-rs](https:\u002F\u002Fgithub.com\u002Flerouxrgd\u002Fngt-rs) - Rust wrappers for NGT approximate nearest neighbor search\n- [granne\u002Fgranne](https:\u002F\u002Fgithub.com\u002Fgranne\u002Fgranne) - Graph-based Approximate Nearest Neighbor Search\n- [u1roh\u002Fkd-tree](https:\u002F\u002Fgithub.com\u002Fu1roh\u002Fkd-tree) - k-dimensional tree in Rust. Fast, simple, and easy to use.\n- [qdrant\u002Fqdrant](https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant) - Qdrant - vector similarity search engine with extended filtering support\n- [rust-cv\u002Fhwt](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fhwt) - Hamming Weight Tree from the paper \"Online Nearest Neighbor Search in Hamming Space\"\n- [fulara\u002Fkdtree-rust](https:\u002F\u002Fgithub.com\u002Ffulara\u002Fkdtree-rust) - kdtree implementation for rust.\n- [mrhooray\u002Fkdtree-rs](https:\u002F\u002Fgithub.com\u002Fmrhooray\u002Fkdtree-rs) - K-dimensional tree in Rust for fast geospatial indexing and lookup\n- [kornelski\u002Fvpsearch](https:\u002F\u002Fgithub.com\u002Fkornelski\u002Fvpsearch) - C library for finding nearest (most similar) element in a set\n- [petabi\u002Fpetal-neighbors](https:\u002F\u002Fgithub.com\u002Fpetabi\u002Fpetal-neighbors) - Nearest neighbor search algorithms including a ball tree and a vantage point tree.\n- [ritchie46\u002Flsh-rs](https:\u002F\u002Fgithub.com\u002Fritchie46\u002Flsh-rs) - Locality Sensitive Hashing in Rust with Python bindings\n- [kampersanda\u002Fmih-rs](https:\u002F\u002Fgithub.com\u002Fkampersanda\u002Fmih-rs) - Rust implementation of multi-index hashing for neighbor searches on 64-bit codes in the Hamming space\n\n\n# Reinforcement Learning\n\n- [taku-y\u002Fborder](https:\u002F\u002Fgithub.com\u002Ftaku-y\u002Fborder) - Border is a reinforcement learning library in Rust.\n- [NivenT\u002FREnforce](https:\u002F\u002Fgithub.com\u002FNivenT\u002FREnforce) - Reinforcement learning library written in Rust\n- [edlanglois\u002Frelearn](https:\u002F\u002Fgithub.com\u002Fedlanglois\u002Frelearn) - Reinforcement learning with Rust\n- [tspooner\u002Frsrl](https:\u002F\u002Fgithub.com\u002Ftspooner\u002Frsrl) - A fast, safe and easy to use reinforcement learning framework in Rust.\n- [milanboers\u002Frurel](https:\u002F\u002Fgithub.com\u002Fmilanboers\u002Frurel) - Flexible, reusable reinforcement learning (Q learning) implementation in Rust\n- [Ragnaroek\u002Fbandit](https:\u002F\u002Fgithub.com\u002FRagnaroek\u002Fbandit) - Bandit Algorithms in Rust\n- [MrRobb\u002Fgym-rs](https:\u002F\u002Fgithub.com\u002Fmrrobb\u002Fgym-rs) - OpenAI Gym bindings for Rust\n\n\n# Supervised Learning Model\n\n- [tomtung\u002Fomikuji](https:\u002F\u002Fgithub.com\u002Ftomtung\u002Fomikuji) - An efficient implementation of Partitioned Label Trees & its variations for extreme multi-label classification\n- [shadeMe\u002Fliblinear-rs](https:\u002F\u002Fgithub.com\u002Fshademe\u002Fliblinear-rs) - Rust language bindings for the LIBLINEAR C\u002FC++ library.\n- [messense\u002Fcrfsuite-rs](https:\u002F\u002Fgithub.com\u002Fmessense\u002Fcrfsuite-rs) - Rust binding to crfsuite\n- [ralfbiedert\u002Fffsvm-rust](https:\u002F\u002Fgithub.com\u002Fralfbiedert\u002Fffsvm-rust) - FFSVM stands for \"Really Fast Support Vector Machine\"\n- [zenoxygen\u002Fbayespam](https:\u002F\u002Fgithub.com\u002Fzenoxygen\u002Fbayespam) - A simple bayesian spam classifier written in Rust.\n- [Rui_Vieira\u002Fnaive-bayesnaive-bayes](https:\u002F\u002Fgitlab.com\u002Fruivieira\u002Fnaive-bayes) - A Naive Bayes classifier written in Rust.\n- [Rui_Vieira\u002Frandom-forests](https:\u002F\u002Fgitlab.com\u002Fruivieira\u002Frandom-forests) - A Rust library for Random Forests.\n- [sile\u002Frandomforest](https:\u002F\u002Fgithub.com\u002Fsile\u002Frandomforest) - A random forest implementation in Rust\n- [tomtung\u002Fcraftml-rs](https:\u002F\u002Fgithub.com\u002Ftomtung\u002Fcraftml-rs) - A Rust🦀 implementation of CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning\n- [nkaush\u002Fnaive-bayes-rs](https:\u002F\u002Fgithub.com\u002Fnkaush\u002Fnaive-bayes-rs) - A Rust library with homemade machine learning models to classify the MNIST dataset. Built in an attempt to get familiar with advanced Rust concepts.\n\n\n# Unsupervised Learning & Clustering Model\n\n- [frjnn\u002Fbhtsne](https:\u002F\u002Fgithub.com\u002Ffrjnn\u002Fbhtsne) - Barnes-Hut t-SNE implementation written in Rust.\n- [vaaaaanquish\u002Flabel-propagation-rs](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Flabel-propagation-rs) - Label Propagation Algorithm by Rust. Label propagation (LP) is graph-based semi-supervised learning (SSL). LGC and CAMLP have been implemented.\n- [nmandery\u002Fextended-isolation-forest](https:\u002F\u002Fgithub.com\u002Fnmandery\u002Fextended-isolation-forest) - Rust port of the extended isolation forest algorithm for anomaly detection\n- [avinashshenoy97\u002FRusticSOM](https:\u002F\u002Fgithub.com\u002Favinashshenoy97\u002FRusticSOM) - Rust library for Self Organising Maps (SOM).\n- [diffeo\u002Fkodama](https:\u002F\u002Fgithub.com\u002Fdiffeo\u002Fkodama) - Fast hierarchical agglomerative clustering in Rust.\n- [kno10\u002Frust-kmedoids](https:\u002F\u002Fgithub.com\u002Fkno10\u002Frust-kmedoids) - k-Medoids clustering in Rust with the FasterPAM algorithm\n- [petabi\u002Fpetal-clustering](https:\u002F\u002Fgithub.com\u002Fpetabi\u002Fpetal-clustering) - DBSCAN and OPTICS clustering algorithms.\n- [savish\u002Fdbscan](https:\u002F\u002Fgithub.com\u002Fsavish\u002Fdbscan) - A naive DBSCAN implementation in Rust\n- [gu18168\u002FDBSCANSD](https:\u002F\u002Fgithub.com\u002Fgu18168\u002FDBSCANSD) - Rust implementation for DBSCANSD, a trajectory clustering algorithm.\n- [lazear\u002Fdbscan](https:\u002F\u002Fgithub.com\u002Flazear\u002Fdbscan) - Dependency free implementation of DBSCAN clustering in Rust\n- [whizsid\u002Fkddbscan-rs](https:\u002F\u002Fgithub.com\u002Fwhizsid\u002Fkddbscan-rs) - A rust library inspired by kDDBSCAN clustering algorithm\n- [Sauro98\u002Fappr_dbscan_rust](https:\u002F\u002Fgithub.com\u002FSauro98\u002Fappr_dbscan_rust) - Program implementing the approximate version of DBSCAN introduced by Gan and Tao\n- [quietlychris\u002Fdensity_clusters](https:\u002F\u002Fgithub.com\u002Fquietlychris\u002Fdensity_clusters) - A naive density-based clustering algorithm written in Rust\n- [milesgranger\u002Fgap_statistic](https:\u002F\u002Fgithub.com\u002Fmilesgranger\u002Fgap_statistic) - Dynamically get the suggested clusters in the data for unsupervised learning.\n- [genbattle\u002Frkm](https:\u002F\u002Fgithub.com\u002Fgenbattle\u002Frkm) - Generic k-means implementation written in Rust\n- [selforgmap\u002Fsom-rust](https:\u002F\u002Fgithub.com\u002Fselforgmap\u002Fsom-rust) - Self Organizing Map (SOM) is a type of Artificial Neural Network (ANN) that is trained using an unsupervised, competitive learning to produce a low dimensional, discretized representation (feature map) of higher dimensional data.\n\n\n# Statistical Model\n\n- [Redpoll\u002Fchangepoint](https:\u002F\u002Fgitlab.com\u002FRedpoll\u002Fchangepoint) - Includes the following change point detection algorithms: Bocpd -- Online Bayesian Change Point Detection Reference. BocpdTruncated -- Same as Bocpd but truncated the run-length distribution when those lengths are unlikely.\n- [krfricke\u002Farima](https:\u002F\u002Fgithub.com\u002Fkrfricke\u002Farima) - ARIMA modelling for Rust\n- [Daingun\u002Fautomatica](https:\u002F\u002Fgitlab.com\u002Fdaingun\u002Fautomatica) - Automatic Control Systems Library\n- [rbagd\u002Frust-linearkalman](https:\u002F\u002Fgithub.com\u002Frbagd\u002Frust-linearkalman) - Kalman filtering and smoothing in Rust\n- [sanity\u002Fpair_adjacent_violators](https:\u002F\u002Fgithub.com\u002Fsanity\u002Fpair_adjacent_violators) - An implementation of the Pair Adjacent Violators algorithm for isotonic regression in Rust\n\n\n# Evolutionary Algorithm\n\n- [martinus\u002Fdifferential-evolution-rs](https:\u002F\u002Fgithub.com\u002Fmartinus\u002Fdifferential-evolution-rs) - Generic Differential Evolution for Rust\n- [innoave\u002Fgenevo](https:\u002F\u002Fgithub.com\u002Finnoave\u002Fgenevo) - Execute genetic algorithm (GA) simulations in a customizable and extensible way.\n- [Jeffail\u002Fspiril](https:\u002F\u002Fgithub.com\u002FJeffail\u002Fspiril) - Rust library for genetic algorithms\n- [sotrh\u002Frust-genetic-algorithm](https:\u002F\u002Fgithub.com\u002Fsotrh\u002Frust-genetic-algorithm) - Example of a genetic algorithm in Rust and Python\n- [willi-kappler\u002Fdarwin-rs](https:\u002F\u002Fgithub.com\u002Fwilli-kappler\u002Fdarwin-rs) - darwin-rs, evolutionary algorithms with rust\n\n\n# Reference\n\n## Nearby Projects\n\n- [Are we learning yet?](http:\u002F\u002Fwww.arewelearningyet.com\u002F), A work-in-progress to catalog the state of machine learning in Rust\n- [e-tony\u002Fbest-of-ml-rust](https:\u002F\u002Fgithub.com\u002Fe-tony\u002Fbest-of-ml-rust), A ranked list of awesome machine learning Rust libraries\n- [The Best 51 Rust Machine learning Libraries](https:\u002F\u002Frustrepo.com\u002Fcatalog\u002Frust-machine-learning_newest_1), RustRepo\n- [rust-unofficial\u002Fawesome-rust](https:\u002F\u002Fgithub.com\u002Frust-unofficial\u002Fawesome-rust), A curated list of Rust code and resources\n- [Top 16 Rust Machine learning Projects](https:\u002F\u002Fwww.libhunt.com\u002Fl\u002Frust\u002Ft\u002Fmachine-learning), Open-source Rust projects categorized as Machine learning\n- [39+ Best Rust Machine learning frameworks, libraries, software and resourcese](https:\u002F\u002Freposhub.com\u002Frust\u002Fmachine-learning), ReposHub\n\n\n## Blogs\n\n### Introduction\n\n- [About Rust’s Machine Learning Community](https:\u002F\u002Fmedium.com\u002F@autumn_eng\u002Fabout-rust-s-machine-learning-community-4cda5ec8a790#.hvkp56j3f), Medium, 2016\u002F1\u002F6, Autumn Engineering\n- [Rust vs Python: Technology And Business Comparison](https:\u002F\u002Fwww.ideamotive.co\u002Fblog\u002Frust-vs-python-technology-and-business-comparison), 2021\u002F3\u002F4, Miłosz Kaczorowski\n- [I wrote one of the fastest DataFrame libraries](https:\u002F\u002Fwww.ritchievink.com\u002Fblog\u002F2021\u002F02\u002F28\u002Fi-wrote-one-of-the-fastest-dataframe-libraries), 2021\u002F2\u002F28, Ritchie Vink \n- [Polars: The fastest DataFrame library you've never heard of](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2021\u002F06\u002Fpolars-the-fastest-dataframe-library-youve-never-heard-of) 2021\u002F1\u002F19, Analytics Vidhya \n- [Data Manipulation: Polars vs Rust](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdata-manipulation-polars-vs-rust--3def44c8), 2021\u002F3\u002F13, Xavier Tao\n- [State of Machine Learning in Rust – Ehsan's Blog](https:\u002F\u002Fehsanmkermani.com\u002F2019\u002F05\u002F13\u002Fstate-of-machine-learning-in-rust\u002F), 2019\u002F5\u002F13, Published by Ehsan\n- [Ritchie Vink, Machine Learning Engineer, writes Polars, one of the fastest DataFrame libraries in Python and Rust](https:\u002F\u002Fwww.xomnia.com\u002Fpost\u002Fritchie-vink-writes-polars-one-of-the-fastest-dataframe-libraries-in-python-and-rust\u002F), Xomnia, 2021\u002F5\u002F11\n- [Quickwit: A highly cost-efficient search engine in Rust](https:\u002F\u002Fquickwit.io\u002Fblog\u002Fquickwit-first-release\u002F), 2021\u002F7\u002F13, quickwit, PAUL MASUREL\n- [Data Manipulation: Polars vs Rust](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdata-manipulation-polars-vs-rust--3def44c8), 2021\u002F3\u002F13, Xavier Tao\n- [Check out Rust in Production](https:\u002F\u002Fserokell.io\u002Fblog\u002Frust-in-production-qovery), 2021\u002F8\u002F10, Qovery, @serokell\n- [Why I started Rust instead of stick to Python](https:\u002F\u002Fmedium.com\u002Fgeekculture\u002Fwhy-i-started-rust-instead-of-stick-to-python-626bab07479a), 2021\u002F9\u002F26, Medium, Geek Culture, Marshal SHI\n\n\n### Tutorial\n\n- [Rust Machine Learning Book](https:\u002F\u002Frust-ml.github.io\u002Fbook\u002Fchapter_1.html), Examples of KMeans and DBSCAN with linfa-clustering\n- [Artificial Intelligence and Machine Learning – Practical Rust Projects: Building Game, Physical Computing, and Machine Learning Applications – Dev Guis ](http:\u002F\u002Fdevguis.com\u002F6-artificial-intelligence-and-machine-learning-practical-rust-projects-building-game-physical-computing-and-machine-learning-applications.html), 2021\u002F5\u002F19\n- [Machine learning in Rust using Linfa](https:\u002F\u002Fblog.logrocket.com\u002Fmachine-learning-in-rust-using-linfa\u002F), LogRocket Blog, 2021\u002F4\u002F30, Timeular, Mario Zupan, Examples of LogisticRegression\n- [Machine Learning in Rust, Smartcore](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-smartcore-2f472d1ce83), Medium, The Startup, 2021\u002F1\u002F15, [Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F), Examples of LinerRegression, Random Forest Regressor, and K-Fold\n- [Machine Learning in Rust, Logistic Regression](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-logistic-regression-74d6743df161), Medium, The Startup, 2021\u002F1\u002F6, [Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F)\n- [Machine Learning in Rust, Linear Regression](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-linear-regression-edef3fb65f93), Medium, The Startup, 2020\u002F12\u002F16, [Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F)\n- [Machine Learning in Rust](https:\u002F\u002Fathemathmo.github.io\u002F2016\u002F03\u002F07\u002Frusty-machine.html), 2016\u002F3\u002F7, James, Examples of LogisticRegressor\n- [Machine Learning and Rust (Part 1): Getting Started!](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-1-getting-started-745885771bc2), Level Up Coding, 2021\u002F1\u002F9, Stefano Bosisio \n- [Machine Learning and Rust (Part 2): Linear Regression](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-2-linear-regression-d3b820ed28f9), Level Up Coding, 2021\u002F6\u002F15, Stefano Bosisio \n- [Machine Learning and Rust (Part 3): Smartcore, Dataframe, and Linear Regression](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-3-smartcore-dataframe-and-linear-regression-10451fdc2e60), Level Up Coding, 2021\u002F7\u002F1, Stefano Bosisio \n- [Tensorflow Rust Practical Part 1](https:\u002F\u002Fwww.programmersought.com\u002Farticle\u002F18696273900\u002F), Programmer Sought, 2018\n- [A Machine Learning introduction to ndarray](https:\u002F\u002Fbarcelona.rustfest.eu\u002Fsessions\u002Fmachine-learning-ndarray), RustFest 2019, 2019\u002F11\u002F12, [Luca Palmieri](https:\u002F\u002Fgithub.com\u002FLukeMathWalker)\n- [Simple Linear Regression from scratch in Rust](https:\u002F\u002Fcheesyprogrammer.com\u002F2018\u002F12\u002F13\u002Fsimple-linear-regression-from-scratch-in-rust\u002F), Web Development, Software Architecture, Algorithms and more, 2018\u002F12\u002F13, philipp\n- [Interactive Rust in a REPL and Jupyter Notebook with EVCXR](https:\u002F\u002Fdepth-first.com\u002Farticles\u002F2020\u002F09\u002F21\u002Finteractive-rust-in-a-repl-and-jupyter-notebook-with-evcxr\u002F), Depth-First, 2020\u002F9\u002F21, Richard L. Apodaca\n- [Rust for Data Science: Tutorial 1](https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j), dev, 2021\u002F8\u002F25, Davide Del Papa\n- [petgraph_review](https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html), 2019\u002F10\u002F11, Timothy Hobbs\n- [Rust for ML. Rust](https:\u002F\u002Fmedium.com\u002Ftempus-ex\u002Frust-for-ml-fba0421b0959), Medium, Tempus Ex, 2021\u002F8\u002F1, Michael Naquin\n- [Adventures in Drone Photogrammetry Using Rust and Machine Learning (Image Segmentation with linfa and DBSCAN)](http:\u002F\u002Fcmoran.xyz\u002Fwriting\u002Fadventures_in_photogrammetry), 2021\u002F11\u002F14, CHRISTOPHER MORAN\n\n\n### Apply\n\n- [Deep Learning in Rust: baby steps](https:\u002F\u002Fmedium.com\u002F@tedsta\u002Fdeep-learning-in-rust-7e228107cccc), Medium,  2016\u002F2\u002F2, Theodore DeRego\n- [A Rust SentencePiece implementation](https:\u002F\u002Fguillaume-be.github.io\u002F2020-05-30\u002Fsentence_piece), Rust NLP tales, 2020\u002F5\u002F30\n- [Accelerating text generation with Rust](https:\u002F\u002Fguillaume-be.github.io\u002F2020-11-21\u002Fgeneration_benchmarks), Rust NLP tales, 2020\u002F11\u002F21\n- [A Simple Text Summarizer written in Rust](https:\u002F\u002Ftowardsdatascience.com\u002Fa-simple-text-summarizer-written-in-rust-4df05f9327a5), Towards Data Science, 2020\u002F11\u002F24, [Charles Chan](https:\u002F\u002Fchancharles.medium.com\u002F), Examples of Text Sentence Vector, Cosine Distance and PageRank\n- [Extracting deep learning image embeddings in Rust](https:\u002F\u002Flogicai.io\u002Fblog\u002Fextracting-image-embeddings\u002F), RecoAI, 2021\u002F6\u002F1, Paweł Jankiewic, Examples of ONNX\n- [Deep Learning in Rust with GPU](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdeep-learning-in-rust-with-gpu--26c53a7f), 2021\u002F7\u002F30, Xavier Tao\n- [tch-rs pretrain example - Docker for PyTorch rust bindings tch-rs. Example of pretrain model](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Ftch-rs-pretrain-example-docker), 2021\u002F8\u002F15, vaaaaanquish\n- [Rust ANN search Example - Image search example by approximate nearest-neighbor library In Rust](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Frust-ann-search-example), 2021\u002F8\u002F15, vaaaaanquish\n- [dzamkov\u002Fdeep-learning-test - Implementing deep learning in Rust using just a linear algebra library (nalgebra)](https:\u002F\u002Fgithub.com\u002Fdzamkov\u002Fdeep-learning-test), 2021\u002F8\u002F30, dzamkov\n- [vaaaaanquish\u002Frust-machine-learning-api-example - The axum example that uses resnet224 to infer images received in base64 and returns the results.](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Frust-machine-learning-api-example), 2021\u002F9\u002F7, vaaaaanquish\n- [Rust for Machine Learning: Benchmarking Performance in One-shot - A Rust implementation of Siamese Neural Networks for One-shot Image Recognition for benchmarking performance and results](https:\u002F\u002Futmist.gitlab.io\u002Fprojects\u002Frust-ml-oneshot\u002F), UofT Machine Intelligence Student Team\n- [Why Wallaroo Moved From Pony To Rust](https:\u002F\u002Fwallarooai.medium.com\u002Fwhy-wallaroo-moved-from-pony-to-rust-292e7339fc34), 2021\u002F8\u002F19, Wallaroo.ai\n- [epwalsh\u002Frust-dl-webserver - Example of serving deep learning models in Rust with batched prediction](https:\u002F\u002Fgithub.com\u002Fepwalsh\u002Frust-dl-webserver), 2021\u002F11\u002F16, epwalsh\n\n\n### Case study\n\n- [Production users - Rust Programming Language](https:\u002F\u002Fwww.rust-lang.org\u002Fproduction\u002Fusers), by rust-lang.org\n- [Taking ML to production with Rust: a 25x speedup](https:\u002F\u002Fwww.lpalmieri.com\u002Fposts\u002F2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup\u002F), A LEARNING JOURNAL, 2019\u002F12\u002F1, [@algo_luca](https:\u002F\u002Ftwitter.com\u002Falgo_luca)\n- [9 Companies That Use Rust in Production](https:\u002F\u002Fserokell.io\u002Fblog\u002Frust-companies), serokell, 2020\u002F11\u002F18, Gints Dreimanis\n- [Masked Language Model on Wasm, BERT on flontend examples](https:\u002F\u002Fgithub.com\u002Foptim-corp\u002Fmasked-lm-wasm\u002F), optim-corp\u002Fmasked-lm-wasm, 2021\u002F8\u002F27, Optim\n- [Serving TensorFlow with Actix-Web](https:\u002F\u002Fgithub.com\u002Fkykosic\u002Factix-tensorflow-example), kykosic\u002Factix-tensorflow-example\n- [Serving PyTorch with Actix-Web](https:\u002F\u002Fgithub.com\u002Fkykosic\u002Factix-pytorch-example), kykosic\u002Factix-pytorch-example\n\n\n## Discussion\n\n- [Natural Language Processing in Rust : rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002F5jj8vr\u002Fnatural_language_processing_in_rust), 2016\u002F12\u002F6\n- [Future prospect of Machine Learning in Rust Programming Language : MachineLearning](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002F7iz51p\u002Fd_future_prospect_of_machine_learning_in_rust\u002F), 2017\u002F11\u002F11\n- [Interest for NLP in Rust? - The Rust Programming Language Forum](https:\u002F\u002Fusers.rust-lang.org\u002Ft\u002Finterest-for-nlp-in-rust\u002F15331), 2018\u002F1\u002F19\n- [Is Rust good for deep learning and artificial intelligence? - The Rust Programming Language Forum](https:\u002F\u002Fusers.rust-lang.org\u002Ft\u002Fis-rust-good-for-deep-learning-and-artificial-intelligence\u002F22866), 2018\u002F11\u002F18\n- [ndarray vs nalgebra : rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fbtn1cz\u002Fndarray_vs_nalgebra\u002F), 2019\u002F5\u002F28\n- [Taking ML to production with Rust | Hacker News](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=21680965), 2019\u002F12\u002F2\n- [Who is using Rust for Machine learning in production\u002Fresearch? : rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Ffvehyq\u002Fd_who_is_using_rust_for_machine_learning_in\u002F), 2020\u002F4\u002F5\n- [Deep Learning in Rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Figz8iv\u002Fdeep_learning_in_rust\u002F), 2020\u002F8\u002F26\n- [SmartCore, fast and comprehensive machine learning library for Rust! : rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fj1mj1g\u002Fsmartcore_fast_and_comprehensive_machine_learning\u002F), 2020\u002F9\u002F29\n- [Deep Learning in Rust with GPU on ONNX](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002Fouul33\u002Fd_p_deep_learning_in_rust_with_gpu_on_onnx\u002F), 2021\u002F7\u002F31\n- [Rust vs. C++ the main differences between these popular programming languages](https:\u002F\u002Fcodilime.com\u002Fblog\u002Frust-vs-cpp-the-main-differences-between-these-popular-programming-languages\u002F), 2021\u002F8\u002F25\n- [I wanted to share my experience of Rust as a deep learning researcher](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fpft9n9\u002Fi_wanted_to_share_my_experience_of_rust_as_a_deep\u002F), 2021\u002F9\u002F2\n- [How far along is the ML ecosystem with Rust?](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fpoglgg\u002Fhow_far_along_is_the_ml_ecosystem_with_rust\u002F), 2021\u002F9\u002F15\n\n\n## Books\n\n- [Practical Machine Learning with Rust: Creating Intelligent Applications in Rust (English Edition)](https:\u002F\u002Famzn.to\u002F3h7JV8U), 2019\u002F12\u002F10, Joydeep Bhattacharjee\n    - Write machine learning algorithms in Rust\n    - Use Rust libraries for different tasks in machine learning\n    - Create concise Rust packages for your machine learning applications\n    - Implement NLP and computer vision in Rust\n    - Deploy your code in the cloud and on bare metal servers\n    - source code: [Apress\u002Fpractical-machine-learning-w-rust](https:\u002F\u002Fgithub.com\u002FApress\u002Fpractical-machine-learning-w-rust)\n- [DATA ANALYSIS WITH RUST NOTEBOOKS](https:\u002F\u002Fdatacrayon.com\u002Fshop\u002Fproduct\u002Fdata-analysis-with-rust-notebooks\u002F), 2021\u002F9\u002F3, Shahin Rostami\n    - Plotting with Plotters and Plotly\n    - Operations with ndarray\n    - Descriptive Statistics\n    - Interactive Diagram\n    - Visualisation of Co-occurring Types\n    - download source code and dataset\n    - full text\n        - [https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F](https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F)\n\n\n## Movie\n\n- [The \u002Fr\u002Fplayrust Classifier: Real World Rust Data Science](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lY10kTcM8ek), RustConf 2016, 2016\u002F10\u002F05, Suchin Gururangan & Colin O'Brien\n- [Building AI Units in Rust](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UHFlKAmANJg), FOSSASIA 2018, 2018\u002F3\u002F25, Vigneshwer Dhinakaran \n- [Python vs Rust for Simulation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kytvDxxedWY), EuroPython 2019, 2019\u002F7\u002F10, Alisa Dammer\n- [Machine Learning is changing - is Rust the right tool for the job?](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=odI_LY8AIqo), RustLab 2019, 2019\u002F10\u002F31, Luca Palmieri\n- [Using TensorFlow in Embedded Rust](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DUVE86yTfKU), 2020\u002F09\u002F29, Ferrous Systems GmbH, Richard Meadows\n- [Writing the Fastest GBDT Library in Rust](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=D1NAREuicNs), 2021\u002F09\u002F16, RustConf 2021, Isabella Tromba\n\n\n## PodCast\n\n- DATA SCIENCE AT HOME\n    - [Rust and machine learning #1 (Ep. 107)](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-1-ep-107\u002F)\n    - [Rust and machine learning #2 with Luca Palmieri (Ep. 108)](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-2-with-luca-palmieri-ep-108\u002F)\n    - [Rust and machine learning #3 with Alec Mocatta (Ep. 109)](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-3-with-alec-mocatta-ep-109\u002F)\n    - [Rust and machine learning #4: practical tools (Ep. 110)](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-4-practical-tools-ep-110\u002F)\n    - [Machine Learning in Rust: Amadeus with Alec Mocatta (Ep. 127)](https:\u002F\u002Fdatascienceathome.com\u002Fmachine-learning-in-rust-amadeus-with-alec-mocatta-rb-ep-127\u002F)\n    - [Rust and deep learning with Daniel McKenna (Ep. 135)](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-deep-learning\u002F)\n    - [Is Rust flexible enough for a flexible data model? (Ep. 137)](https:\u002F\u002Fdatascienceathome.com\u002Fis-rust-flexible-enough-for-a-flexible-data-model-ep-137\u002F)\n    - [Pandas vs Rust (Ep. 144)](https:\u002F\u002Fdatascienceathome.com\u002Fpandas-vs-rust-ep-144\u002F)\n    - [Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)](https:\u002F\u002Fdatascienceathome.com\u002Fapache-arrow-ballista-and-big-data-in-rust-with-andy-grove-ep-145\u002F)\n    - [Polars: the fastest dataframe crate in Rust (Ep. 146)](https:\u002F\u002Fdatascienceathome.com\u002Fpolars-the-fastest-dataframe-crate-in-rust-ep-146\u002F)\n    - [Apache Arrow, Ballista and Big Data in Rust with Andy Grove RB (Ep. 160)](https:\u002F\u002Fdatascienceathome.com\u002Fapache-arrow-ballista-and-big-data-in-rust-with-andy-grove-rb-ep-160\u002F)\n\n\n## Paper\n\n- [End-to-end NLP Pipelines in Rust](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.nlposs-1.4.pdf), Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 20–25 Virtual Conference, 2020\u002F11\u002F19, Guillaume Becquin\n\n\n# How to contribute\n\nPlease just update the README.md.\n\nIf you update this README.md, CI will be executed automatically.\nAnd the website will also be updated.\n\n\n# Thanks\n\nThanks for all the projects.\n\n[https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning)\n","![arml](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fvaaaaanquish_Awesome-Rust-MachineLearning_readme_e4d1c743a631.png)\n\n这个仓库是一个用 Rust 编写的机器学习库列表。\n它汇集了 GitHub 仓库、博客、书籍、电影、讨论和论文等内容。\n该仓库面向那些考虑从 Python 迁移到 Rust 的开发者。🦀🐍\n\n仓库按基础库和算法类别进行了划分。\n此外，还包含一些已停止维护的库以及小型库。\n对代码中实用的部分进行了注释说明，\n并对每个类别中的优秀库进行了点评。\n\n通过这些资源，我们可以找到更优的 Rust 在机器学习领域的使用方式。\n\n\n- [网站（英文）](https:\u002F\u002Fvaaaaanquish.github.io\u002FAwesome-Rust-MachineLearning)\n- [GitHub（英文）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning\u002Fblob\u002Fmain\u002FREADME.md)\n- [GitHub（日文）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning\u002Fblob\u002Fmain\u002FREADME.ja.md)\n\n\n# 目录\n\n- [支持工具](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#support-tools)\n    - [Jupyter Notebook](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#jupyter-notebook)\n    - [图表绘制](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graph-plot)\n    - [向量](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#vector)\n    - [数据框](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#dataframe)\n    - [图像处理](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#image-processing)\n    - [自然语言处理（预处理）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#natural-language-processing-preprocessing)\n    - [图模型](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graphical-modeling)\n    - [接口、流水线与 AutoML](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#interface--pipeline--automl)\n    - [工作流](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#workflow)\n    - [GPU](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#gpu)\n- [综合类（类似 sklearn）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#comprehensive-like-sklearn)\n- [综合类（统计学）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#comprehensive-statistics)\n- [梯度提升](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#gradient-boosting)\n- [深度神经网络](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#deep-neural-network)\n- [图模型](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#graph-model)\n- [自然语言处理（模型）](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#natural-language-processing-model)\n- [推荐系统](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#recommendation)\n- [信息检索](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#information-retrieval)\n    - [全文搜索](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#full-text-search)\n    - [近邻搜索](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#nearest-neighbor-search)\n- [强化学习](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#reinforcement-learning)\n- [监督学习](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#supervised-learning-model)\n- [无监督学习与聚类模型](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#unsupervised-learning--clustering-model)\n- [统计模型](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#statistical-model)\n- [进化算法](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#evolutionary-algorithm)\n- [参考资料](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#reference)\n    - [相关项目](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#nearby-projects)\n    - [博客](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#blogs)\n        - [入门](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#introduction)\n        - [教程](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#tutorial)\n        - [应用](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#apply)\n        - [案例研究](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#case-study)\n    - [讨论](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#discussion)\n    - [书籍](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#books)\n    - [电影](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#movie)\n    - [播客](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#podcast)\n    - [论文](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#paper)\n- [致谢](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning#thanks)\n\n\n\n# 支持工具\n\n\n## Jupyter Notebook\n\n`evcxr` 可以作为 Jupyter 内核或 REPL 使用，非常有助于学习和验证。\n\n- [google\u002Fevcxr](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fevcxr) - Rust 的评估上下文。\n- [emakryo\u002Frustdef](https:\u002F\u002Fgithub.com\u002Femakryo\u002Frustdef) - Rust 的 Jupyter 扩展。\n- [murarth\u002Frusti](https:\u002F\u002Fgithub.com\u002Fmurarth\u002Frusti) - Rust 编程语言的 REPL。\n\n## 图表绘制\n\n目前可以尝试使用 `plotters`。\n\n- [38\u002Fplotters](https:\u002F\u002Fgithub.com\u002F38\u002Fplotters) - 一个用于高质量数据可视化的 Rust 绘图库，支持 WASM 和原生环境，可静态生成也可实时绘制 🦀 📈🚀\n- [igiagkiozis\u002Fplotly](https:\u002F\u002Fgithub.com\u002Figiagkiozis\u002Fplotly) - Rust 版的 Plotly\n- [milliams\u002Fplotlib](https:\u002F\u002Fgithub.com\u002Fmilliams\u002Fplotlib) - Rust 数据绘图库\n- [tiby312\u002Fpoloto](https:\u002F\u002Fgithub.com\u002Ftiby312\u002Fpoloto) - 一个简单的 2D 绘图库，可将图表输出为 SVG 格式，并可通过 CSS 进行样式定制。\n- [askanium\u002Frustplotlib](https:\u002F\u002Fgithub.com\u002Faskanium\u002Frustplotlib) - 受 D3.js 启发的纯 Rust 可视化库\n- [SiegeLord\u002FRustGnuplot](https:\u002F\u002Fgithub.com\u002FSiegeLord\u002FRustGnuplot) - 基于 Gnuplot 的 Rust 绘图库。\n- [saona-raimundo\u002Fpreexplorer](https:\u002F\u002Fgithub.com\u002Fsaona-raimundo\u002Fpreexplorer) - 轻松将 Rust 中的绘图过程外部化到 Gnuplot。\n- [procyon-rs\u002Fvega_lite_4.rs](https:\u002F\u002Fgithub.com\u002Fprocyon-rs\u002Fvega_lite_4.rs) - Vega-Lite v4 的 Rust API\n    - [procyon-rs\u002Fshowata](https:\u002F\u002Fgithub.com\u002Fprocyon-rs\u002Fshowata) - 一个用于在浏览器或 evcxr_jupyter 中以表格、图表等形式展示数据的库。\n- [coder543\u002Fdataplotlib](https:\u002F\u002Fgithub.com\u002Fcoder543\u002Fdataplotlib) - Rust 科学绘图库\n- [shahinrostami\u002Fchord_rs](https:\u002F\u002Fgithub.com\u002Fshahinrostami\u002Fchord_rs) - 用于创建精美交互式弦图的 Rust crate。专业版可在 https:\u002F\u002Fm8.fyi\u002Fchord 获取。\n\n\nASCII 线形图：\n\n- [loony-bean\u002Ftextplots-rs](https:\u002F\u002Fgithub.com\u002Floony-bean\u002Ftextplots-rs) - Rust 终端绘图库\n- [orhanbalci\u002Frasciigraph](https:\u002F\u002Fgithub.com\u002Forhanbalci\u002Frasciigraph) - 无依赖的 Rust crate，用于在命令行应用中轻松绘制轻量级 ASCII 线形图 ╭┈╯。\n- [jakobhellermann\u002Fpiechart](https:\u002F\u002Fgithub.com\u002Fjakobhellermann\u002Fpiechart) - 一个在终端中绘制精美饼图的 Rust crate\n- [milliams\u002Fplot](https:\u002F\u002Fgithub.com\u002Fmilliams\u002Fplot) - 用 Rust 编写的命令行绘图工具\n\n\n示例：\n\n- Plotters 开发者指南 - 绘图开发者指南 [https:\u002F\u002Fplotters-rs.github.io\u002Fbook\u002Fintro\u002Fintroduction.html](https:\u002F\u002Fplotters-rs.github.io\u002Fbook\u002Fintro\u002Fintroduction.html)\n- Plotly.rs - Plotly.rs 书籍 [https:\u002F\u002Figiagkiozis.github.io\u002Fplotly\u002Fcontent\u002Fplotly_rs.html](https:\u002F\u002Figiagkiozis.github.io\u002Fplotly\u002Fcontent\u002Fplotly_rs.html)\n- petgraph_review [https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html](https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html)\n- evcxr-jupyter 集成 [https:\u002F\u002Fplotters-rs.github.io\u002Fplotters-doc-data\u002Fevcxr-jupyter-integration.html](https:\u002F\u002Fplotters-rs.github.io\u002Fplotters-doc-data\u002Fevcxr-jupyter-integration.html)\n- Rust 数据科学教程 1 - DEV 社区 [https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j](https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j)\n- 序言 | Data Crayon [https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F](https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F)\n- 使用 Rust 绘制 SVG 图表 [https:\u002F\u002Fcetra3.github.io\u002Fblog\u002Fdrawing-svg-graphs-rust\u002F](使用 Rust 绘制 SVG 图表 https:\u002F\u002Fcetra3.github.io\u002Fblog\u002Fdrawing-svg-graphs-rust\u002F)\n\n\n## 向量\n\n大多数情况下会使用 `ndarray` 或 `std::vec`。\n\n此外，也可以关注 `nalgebra`。当矩阵的大小已知时，它是一个不错的选择。\n参见：[ndarray 与 nalgebra 的比较 - Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fbtn1cz\u002Fndarray_vs_nalgebra\u002F)\n\n- [dimforge\u002Fnalgebra](https:\u002F\u002Fgithub.com\u002Fdimforge\u002Fnalgebra) - Rust 的线性代数库。\n- [rust-ndarray\u002Fndarray](https:\u002F\u002Fgithub.com\u002Frust-ndarray\u002Fndarray) - ndarray：具有数组视图、多维切片和高效操作的 N 维数组\n- [AtheMathmo\u002Frulinalg](https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frulinalg) - 用 Rust 编写的线性代数库\n- [arrayfire\u002Farrayfire-rust](https:\u002F\u002Fgithub.com\u002Farrayfire\u002Farrayfire-rust) - ArrayFire 的 Rust 封装\n- [bluss\u002Farrayvec](https:\u002F\u002Fgithub.com\u002Fbluss\u002Farrayvec) - 固定容量的向量。（Rust）\n- [vbarrielle\u002Fsprs](https:\u002F\u002Fgithub.com\u002Fvbarrielle\u002Fsprs) - Rust 的稀疏线性代数库\n- [liborty\u002Frstats](https:\u002F\u002Fgithub.com\u002Fliborty\u002Frstats) - Rust 统计与向量代数库\n- [PyO3\u002Frust-numpy](https:\u002F\u002Fgithub.com\u002FPyO3\u002Frust-numpy) - 基于 PyO3 的 NumPy C-API Rust 绑定\n\n\n## 数据框\n\n目前可以尝试使用 `polars`。`datafusion` 也是一个不错的选择。\n\n- [ritchie46\u002Fpolars](https:\u002F\u002Fgithub.com\u002Fritchie46\u002Fpolars) - Rust 数据框库\n- [apache\u002Farrow](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow-rs) - 内存中的列式格式，基于 Rust 实现。\n- [apache\u002Farrow-datafusion](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow-datafusion) - Apache Arrow 的 DataFusion 和 Ballista 查询引擎\n- [milesgranger\u002Fblack-jack](https:\u002F\u002Fgithub.com\u002Fmilesgranger\u002Fblack-jack) - 在 Rust 中进行 DataFrame \u002F Series 数据处理\n- [nevi-me\u002Frust-dataframe](https:\u002F\u002Fgithub.com\u002Fnevi-me\u002Frust-dataframe) - 基于 Apache Arrow 构建的 Rust 数据框实现\n- [kernelmachine\u002Futah](https:\u002F\u002Fgithub.com\u002Fkernelmachine\u002Futah) - Rust 中的数据框结构及操作\n- [sinhrks\u002Fbrassfibre](https:\u002F\u002Fgithub.com\u002Fsinhrks\u002Fbrassfibre) - 提供多类型列存储，类似于 pandas\u002FR 中的 DataFrame\n\n\n## 图像处理\n\n目前可以尝试使用 `image-rs`。其他库中也实现了诸如线性变换之类的算法。\n\n- [image-rs\u002Fimage](https:\u002F\u002Fgithub.com\u002Fimage-rs\u002Fimage) - Rust 中的图像编码与解码\n    - [image-rs\u002Fimageproc](https:\u002F\u002Fgithub.com\u002Fimage-rs\u002Fimageproc) - 图像处理操作\n- [rust-cv\u002Fndarray-image](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fndarray-image) - 允许在 ndarray 类型和图像类型之间进行转换\n- [rust-cv\u002Fcv](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fcv) - Rust CV 单体仓库。包含纯 Rust 依赖项，旨在将 OpenCV、OpenMVG 和 vSLAM 框架的功能封装在一个统一的 API 集合中。\n- [twistedfall\u002Fopencv-rust](https:\u002F\u002Fgithub.com\u002Ftwistedfall\u002Fopencv-rust) - OpenCV 3 和 4 的 Rust 绑定\n- [rustgd\u002Fcgmath](https:\u002F\u002Fgithub.com\u002Frustgd\u002Fcgmath) - 用于计算机图形学的线性代数和数学库。\n- [atomashpolskiy\u002Frustface](https:\u002F\u002Fgithub.com\u002Fatomashpolskiy\u002Frustface) - Rust 编程语言中的人脸检测库\n\n## 自然语言处理（预处理）\n\n- [google-research\u002Fdeduplicate-text-datasets](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fdeduplicate-text-datasets) - 该仓库包含用于去重语言模型数据集的代码，如凯瑟琳·李、达芙妮·伊波利托、安德鲁·尼斯特罗姆、张驰远、道格拉斯·埃克、克里斯·卡利森-伯奇和尼古拉斯·卡林尼在论文《Deduplicating Training Data Makes Language Models Better》中所述。此仓库既包含了用 Rust 编写的 ExactSubstr 去重实现，也包含了我们在论文中用来执行去重并检查结果的 Python 脚本。在即将发布的更新中，我们将添加文件以复现 C4、RealNews、LM1B 和 Wiki-40B-en 数据集的 NearDup 去重版本。\n- [pemistahl\u002Flingua-rs](https:\u002F\u002Fgithub.com\u002Fpemistahl\u002Flingua-rs) - 👄 Rust 生态系统中最准确的自然语言检测库，适用于长短文本。\n- [usamec\u002Fcntk-rs](https:\u002F\u002Fgithub.com\u002Fusamec\u002Fcntk-rs) - Microsoft CNTK 库的封装。\n- [stickeritis\u002Fsticker](https:\u002F\u002Fgithub.com\u002Fstickeritis\u002Fsticker) - 一种基于 LSTM\u002FTransformer\u002F扩张卷积的序列标注器。\n- [tensordot\u002Fsyntaxdot](https:\u002F\u002Fgithub.com\u002Ftensordot\u002Fsyntaxdot) - 神经语法标注器，支持序列标注、词形还原和依存句法分析。\n- [christophertrml\u002Frs-natural](https:\u002F\u002Fgithub.com\u002Fchristophertrml\u002Frs-natural) - Rust 的自然语言处理工具。\n- [bminixhofer\u002Fnnsplit](https:\u002F\u002Fgithub.com\u002Fbminixhofer\u002Fnnsplit) - 语义文本分割。用于句子边界检测、复合句拆分等。\n- [greyblake\u002Fwhatlang-rs](https:\u002F\u002Fgithub.com\u002Fgreyblake\u002Fwhatlang-rs) - Rust 的自然语言检测库。\n- [finalfusion\u002Ffinalfrontier](https:\u002F\u002Fgithub.com\u002Ffinalfusion\u002Ffinalfrontier) - 基于子词的上下文敏感词嵌入。使用 Rust 实现。\n- [bminixhofer\u002Fnlprule](https:\u002F\u002Fgithub.com\u002Fbminixhofer\u002Fnlprule) - 一个用 Rust 编写的快速、低资源消耗的自然语言处理与错误纠正库。\n- [rth\u002Fvtext](https:\u002F\u002Fgithub.com\u002Frth\u002Fvtext) - 使用 Python 绑定的简单 Rust NLP 工具。\n- [tamuhey\u002Ftokenizations](https:\u002F\u002Fgithub.com\u002Ftamuhey\u002Ftokenizations) - 强健且快速的 Rust 和 Python 令牌对齐库。\n- [vgel\u002Ftreebender](https:\u002F\u002Fgithub.com\u002Fvgel\u002Ftreebender) - 一款受 HDPSG 启发、用 Rust 编写的符号化自然语言解析器。\n- [reinfer\u002Fblingfire-rs](https:\u002F\u002Fgithub.com\u002Freinfer\u002Fblingfire-rs) - BlingFire 分词库的 Rust 封装。\n- [CurrySoftware\u002Frust-stemmers](https:\u002F\u002Fgithub.com\u002FCurrySoftware\u002Frust-stemmers) - 多种语言中的常用停用词。\n- [cmccomb\u002Frust-stop-words](https:\u002F\u002Fgithub.com\u002Fcmccomb\u002Frust-stop-words) - 多种语言中的常用停用词。\n- [Freyskeyd\u002Fnlp](https:\u002F\u002Fgithub.com\u002FFreyskeyd\u002Fnlp) - Rust-nlp 是一个使用 Rust 进行自然语言处理算法的库。\n- [Daniel-Liu-c0deb0t\u002Fuwu](https:\u002F\u002Fgithub.com\u002FDaniel-Liu-c0deb0t\u002Fuwu) - 西方最快的文本 uwu 化工具。\n\n\n## 图模型\n\n- [alibaba\u002FGraphScope](https:\u002F\u002Fgithub.com\u002Falibaba\u002FGraphScope) - GraphScope：阿里巴巴的一站式大规模图计算系统。\n- [petgraph\u002Fpetgraph](https:\u002F\u002Fgithub.com\u002Fpetgraph\u002Fpetgraph) - Rust 的图数据结构库。\n- [rs-graph\u002Frs-graph](https:\u002F\u002Fchiselapp.com\u002Fuser\u002Ffifr\u002Frepository\u002Frs-graph\u002Fdoc\u002Frelease\u002FREADME.md) - rs-graph 是一个用于图算法和组合优化的库。\n- [metamolecular\u002Fgamma](https:\u002F\u002Fgithub.com\u002Fmetamolecular\u002Fgamma) - Rust 的图库。\n- [purpleprotocol\u002Fgraphlib](https:\u002F\u002Fgithub.com\u002Fpurpleprotocol\u002Fgraphlib) - 简单但功能强大的 Rust 图库。\n- [yamafaktory\u002Fhypergraph](https:\u002F\u002Fgithub.com\u002Fyamafaktory\u002Fhypergraph) - 超图是一种用于生成有向超图的数据结构库。\n\n## 接口、流水线与 AutoML\n\n- [modelfoxdotdev\u002Fmodelfox](https:\u002F\u002Fgithub.com\u002Fmodelfoxdotdev\u002Fmodelfox) - Modelfox 是一个一体化的自动化机器学习框架。https:\u002F\u002Fgithub.com\u002Fmodelfoxdotdev\u002Fmodelfox\n- [datafuselabs\u002Fdatafuse](https:\u002F\u002Fgithub.com\u002Fdatafuselabs\u002Fdatafuse) - 采用云原生架构的现代实时数据处理与分析 DBMS，使用 Rust 编写。\n- [mstallmo\u002Ftensorrt-rs](https:\u002F\u002Fgithub.com\u002Fmstallmo\u002Ftensorrt-rs) - 用于运行 TensorRT 加速深度学习模型的 Rust 库。\n- [pipehappy1\u002Ftensorboard-rs](https:\u002F\u002Fgithub.com\u002Fpipehappy1\u002Ftensorboard-rs) - 用 Rust 编写 TensorBoard 事件。\n- [ehsanmok\u002Ftvm-rust](https:\u002F\u002Fgithub.com\u002Fehsanmok\u002Ftvm-rust) - TVM 运行时的 Rust 绑定。\n- [vertexclique\u002Forkhon](https:\u002F\u002Fgithub.com\u002Fvertexclique\u002Forkhon) - Orkhon：ML 推理框架及服务器运行时。\n- [xaynetwork\u002Fxaynet](https:\u002F\u002Fgithub.com\u002Fxaynetwork\u002Fxaynet) - Xaynet 是一个通用的联邦机器学习框架，用于构建保护隐私的人工智能应用。\n- [webonnx\u002Fwonnx](https:\u002F\u002Fgithub.com\u002Fwebonnx\u002Fwonnx) - 100% 用 Rust 编写的 GPU 加速 ONNX 推理运行时，专为 Web 构建。\n- [sonos\u002Ftract](https:\u002F\u002Fgithub.com\u002Fsonos\u002Ftract) - 精巧、简洁、自包含的 TensorFlow 和 ONNX 推理引擎。\n- [MegEngine\u002FMegFlow](https:\u002F\u002Fgithub.com\u002FMegEngine\u002FMegFlow) - 针对长尾需求的高效 ML 解决方案。\n\n\n## 工作流\n\n- [substantic\u002Frain](https:\u002F\u002Fgithub.com\u002Fsubstantic\u002Frain) - 大型分布式流水线框架。\n- [timberio\u002Fvector](https:\u002F\u002Fgithub.com\u002Ftimberio\u002Fvector) - 高性能、高可靠性的可观测性数据流水线。\n\n\n## GPU\n\n- [Rust-GPU\u002FRust-CUDA](https:\u002F\u002Fgithub.com\u002FRust-GPU\u002FRust-CUDA) - 一个完全用 Rust 编写并执行极速 GPU 代码的库与工具生态系统。\n- [EmbarkStudios\u002Frust-gpu](https:\u002F\u002Fgithub.com\u002FEmbarkStudios\u002Frust-gpu) - 🐉 让 Rust 成为 GPU 代码的一流语言与生态体系 🚧\n- [termoshtt\u002Faccel](https:\u002F\u002Fgithub.com\u002Ftermoshtt\u002Faccel) - Rust 的 GPGPU 框架。\n- [kmcallister\u002Fglassful](https:\u002F\u002Fgithub.com\u002Fkmcallister\u002Fglassful) - OpenGL 着色语言的 Rust 风格语法。\n- [MaikKlein\u002Frlsl](https:\u002F\u002Fgithub.com\u002FMaikKlein\u002Frlsl) - Rust 到 SPIR-V 的编译器。\n- [japaric-archived\u002Fnvptx](https:\u002F\u002Fgithub.com\u002Fjaparic-archived\u002Fnvptx) - 如何在 NVIDIA GPU 上运行 Rust 代码。\n- [msiglreith\u002Finspirv-rust](https:\u002F\u002Fgithub.com\u002Fmsiglreith\u002Finspirv-rust) - Rust (MIR) → SPIR-V (着色器) 编译器。\n\n# 综合类（类似 scikit-learn）\n\n所有库都支持以下算法。\n\n- 线性回归\n- 逻辑回归\n- K 均值聚类\n- 神经网络\n- 高斯过程回归\n- 支持向量机\n- 高斯混合模型\n- 朴素贝叶斯分类器\n- DBSCAN\n- k 近邻分类器\n- 主成分分析\n- 决策树\n- 支持向量机\n- 朴素贝叶斯\n- 弹性网络\n\n\n目前可以尝试使用 `smartcore` 或 `linfa`。\n\n- [smartcorelib\u002Fsmartcore](https:\u002F\u002Fgithub.com\u002Fsmartcorelib\u002Fsmartcore) - SmartCore 是一个用于机器学习和数值计算的综合性库。该库提供了一系列线性代数、数值计算、优化等方面的工具，能够实现通用、强大且高效的机器学习方法。\n    - LASSO、Ridge、随机森林、LU 分解、QR 分解、奇异值分解、特征值分解等多种算法及指标\n    - https:\u002F\u002Fsmartcorelib.org\u002Fuser_guide\u002Fquick_start.html\n- [rust-ml\u002Flinfa](https:\u002F\u002Fgithub.com\u002Frust-ml\u002Flinfa) - Rust 机器学习框架。\n    - 高斯混合模型聚类、凝聚层次聚类、独立成分分析\n    - https:\u002F\u002Fgithub.com\u002Frust-ml\u002Flinfa#current-state\n- [maciejkula\u002Frustlearn](https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Frustlearn) - Rust 的机器学习 crate\n    - 因子分解机、k 折交叉验证、ndcg\n    - https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Frustlearn#features\n- [AtheMathmo\u002Frusty-machine](https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frusty-machine) - Rust 的机器学习库\n    - 混淆矩阵、交叉验证、准确率、F1 分数、均方误差\n    - https:\u002F\u002Fgithub.com\u002FAtheMathmo\u002Frusty-machine#machine-learning\n- [benjarison\u002Feval-metrics](https:\u002F\u002Fgithub.com\u002Fbenjarison\u002Feval-metrics) - 用于机器学习的评估指标\n    - 多种评估函数\n- [blue-yonder\u002Fvikos](https:\u002F\u002Fgithub.com\u002Fblue-yonder\u002Fvikos) - 用于有监督训练参数化模型的机器学习库\n- [mbillingr\u002Fopenml-rust](https:\u002F\u002Fgithub.com\u002Fmbillingr\u002Fopenml-rust) - Rust 对 http:\u002F\u002Fopenml.org 的接口\n\n\n# 综合类（统计学）\n\n- [statrs-dev\u002Fstatrs](https:\u002F\u002Fgithub.com\u002Fstatrs-dev\u002Fstatrs) - Rust 的统计计算库\n- [rust-ndarray\u002Fndarray-stats](https:\u002F\u002Fgithub.com\u002Frust-ndarray\u002Fndarray-stats) - ndarray 的统计函数库\n- [Axect\u002FPeroxide](https:\u002F\u002Fgithub.com\u002FAxect\u002FPeroxide) - 具有 R、MATLAB 和 Python 语法的 Rust 数值库\n    - 线性代数、函数式编程、自动微分、数值分析、统计学、特殊函数、绘图、数据框\n- [tarcieri\u002Fmicromath](https:\u002F\u002Fgithub.com\u002Ftarcieri\u002Fmicromath) - 嵌入式 Rust 的算术、2D\u002F3D 向量和统计学库\n\n\n# 梯度提升\n\n- [mesalock-linux\u002Fgbdt-rs](https:\u002F\u002Fgithub.com\u002Fmesalock-linux\u002Fgbdt-rs) - MesaTEE GBDT-RS：一个快速且安全的 GBDT 库，支持 Intel SGX 和 ARM TrustZone 等可信执行环境。\n- [davechallis\u002Frust-xgboost](https:\u002F\u002Fgithub.com\u002Fdavechallis\u002Frust-xgboost) - XGBoost 的 Rust 绑定。\n- [vaaaaanquish\u002Flightgbm-rs](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Flightgbm-rs) - LightGBM 的 Rust 绑定\n- [catboost\u002Fcatboost](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost\u002Ftree\u002Fmaster\u002Fcatboost\u002Frust-package) - 一个快速、可扩展、高性能的基于决策树的梯度提升库，用于排序、分类、回归及其他机器学习任务（仅预测）。\n- [Entscheider\u002Fstamm](https:\u002F\u002Fgithub.com\u002Fentscheider\u002Fstamm) - Rust 的通用决策树\n\n\n# 深度神经网络\n\n最常用的有 `TensorFlow 绑定` 和 `PyTorch 绑定`。`tch-rs` 还提供了 torch vision，非常实用。\n\n- [tensorflow\u002Frust](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Frust) - TensorFlow 的 Rust 语言绑定\n- [LaurentMazare\u002Ftch-rs](https:\u002F\u002Fgithub.com\u002FLaurentMazare\u002Ftch-rs) - PyTorch C++ API 的 Rust 绑定。\n- [VasanthakumarV\u002Feinops](https:\u002F\u002Fgithub.com\u002Fvasanthakumarv\u002Feinops) - 用于深度学习张量操作的简化 API\n- [spearow\u002Fjuice](https:\u002F\u002Fgithub.com\u002Fspearow\u002Fjuice) - 黑客的机器学习引擎\n- [neuronika\u002Fneuronika](https:\u002F\u002Fgithub.com\u002Fneuronika\u002Fneuronika) - 纯 Rust 中的张量和动态神经网络。\n- [bilal2vec\u002FL2](https:\u002F\u002Fgithub.com\u002Fbilal2vec\u002FL2) - l2 是一个用 Rust 编写的、具有 PyTorch 风格的快速张量+自动微分库\n- [raskr\u002Frust-autograd](https:\u002F\u002Fgithub.com\u002Fraskr\u002Frust-autograd) - Rust 中的张量和可微分操作（类似于 TensorFlow）\n- [charles-r-earp\u002Fautograph](https:\u002F\u002Fgithub.com\u002Fcharles-r-earp\u002Fautograph) - Rust 的机器学习库\n- [patricksongzy\u002Fcorgi](https:\u002F\u002Fgithub.com\u002Fpatricksongzy\u002Fcorgi) - 一个用于 Rust 的神经网络及张量动态自动微分实现。\n- [JonathanWoollett-Light\u002Fcogent](https:\u002F\u002Fgithub.com\u002FJonathanWoollett-Light\u002Fcogent) - 用 Rust 编写的简单分类神经网络库。\n- [oliverfunk\u002Fdarknet-rs](https:\u002F\u002Fgithub.com\u002Foliverfunk\u002Fdarknet-rs) - Darknet 的 Rust 绑定\n- [jakelee8\u002Fmxnet-rs](https:\u002F\u002Fgithub.com\u002Fjakelee8\u002Fmxnet-rs) - MXNet 的 Rust 版本\n- [jramapuram\u002Fhal](https:\u002F\u002Fgithub.com\u002Fjramapuram\u002Fhal) - 基于 Rust 的跨 GPU 机器学习\n- [primitiv\u002Fprimitiv-rust](https:\u002F\u002Fgithub.com\u002Fprimitiv\u002Fprimitiv-rust) - Primitiv 的 Rust 绑定\n- [chantera\u002Fdynet-rs](https:\u002F\u002Fgithub.com\u002Fchantera\u002Fdynet-rs) - DyNet 的 Rust 语言绑定\n- [millardjn\u002Falumina](https:\u002F\u002Fgithub.com\u002Fmillardjn\u002Falumina) - 一个用于 Rust 的深度学习库\n- [jramapuram\u002Fhal](https:\u002F\u002Fgithub.com\u002Fjramapuram\u002Fhal) - 基于 Rust 的跨 GPU 机器学习\n- [afck\u002Ffann-rs](https:\u002F\u002Fgithub.com\u002Fafck\u002Ffann-rs) - 快速人工神经网络库的 Rust 封装\n- [autumnai\u002Fleaf](https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fleaf) - 黑客的开放机器智能框架。（GPU\u002FCPU）\n- [c0dearm\u002Fmushin](https:\u002F\u002Fgithub.com\u002Fc0dearm\u002Fmushin) - 编译时创建神经网络\n- [tedsta\u002Fdeeplearn-rs](https:\u002F\u002Fgithub.com\u002Ftedsta\u002Fdeeplearn-rs) - Rust 中的神经网络\n- [sakex\u002Fneat-gru-rust](https:\u002F\u002Fgithub.com\u002Fsakex\u002Fneat-gru-rust) - neat-gru\n- [nerosnm\u002Fn2](https:\u002F\u002Fgithub.com\u002Fnerosnm\u002Fn2) - （正在进行中）前馈、反向传播人工神经网络的库实现\n- [Wuelle\u002Fdeep_thought](https:\u002F\u002Fgithub.com\u002FWuelle\u002Fdeep_thought) - Rust 中的神经网络\n- [MikhailKravets\u002FNeuroFlow](https:\u002F\u002Fgithub.com\u002FMikhailKravets\u002FNeuroFlow) - 令人惊叹的深度学习 crate\n- [dvigneshwer\u002Fdeeprust](https:\u002F\u002Fgithub.com\u002Fdvigneshwer\u002Fdeeprust) - Rust 中的机器学习 crate\n- [millardjn\u002Frusty_sr](https:\u002F\u002Fgithub.com\u002Fmillardjn\u002Frusty_sr) - 纯 Rust 中的深度学习超分辨率\n- [coreylowman\u002Fdfdx](https:\u002F\u002Fgithub.com\u002Fcoreylowman\u002Fdfdx) - Rust 中强类型的深度学习\n\n\n# 图模型\n\n- [Synerise\u002Fcleora](https:\u002F\u002Fgithub.com\u002FSynerise\u002Fcleora) - Cleora AI 是一种通用模型，用于高效、可扩展地学习异构关系数据中的稳定且归纳性的实体嵌入。\n- [Pardoxa\u002Fnet_ensembles](https:\u002F\u002Fgithub.com\u002FPardoxa\u002Fnet_ensembles) - 用于随机图集合的 Rust 库\n\n# 自然语言处理（模型）\n\n- [huggingface\u002Ftokenizers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftokenizers\u002Ftree\u002Fmaster\u002Ftokenizers) - 由 Rust 编写的分词器核心库。提供了当前最常用的多种分词算法实现，注重性能与通用性。\n- [guillaume-be\u002Frust-tokenizers](https:\u002F\u002Fgithub.com\u002Fguillaume-be\u002Frust-tokenizers) - Rust 分词器为现代语言模型提供高性能的分词工具，支持 WordPiece、字节对编码（BPE）和 Unigram（SentencePiece）等模型。\n- [guillaume-be\u002Frust-bert](https:\u002F\u002Fgithub.com\u002Fguillaume-be\u002Frust-bert) - 基于 Rust 的原生 NLP 流程及基于 Transformer 的预训练模型（如 BERT、DistilBERT、GPT2 等）。\n- [sno2\u002Fbertml](https:\u002F\u002Fgithub.com\u002Fsno2\u002Fbertml) - 在 Deno 中使用常见的预训练机器学习模型！\n- [cpcdoy\u002Frust-sbert](https:\u002F\u002Fgithub.com\u002Fcpcdoy\u002Frust-sbert) - 句子嵌入模型（sentence-transformers，https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers）的 Rust 移植版本。\n- [vongaisberg\u002Fgpt3_macro](https:\u002F\u002Fgithub.com\u002Fvongaisberg\u002Fgpt3_macro) - 使用 GPT3 Codex 在编译时生成代码的 Rust 宏。\n- [proycon\u002Fdeepfrog](https:\u002F\u002Fgithub.com\u002Fproycon\u002Fdeepfrog) - 基于深度学习的 NLP 工具集。\n- [ferristseng\u002Frust-tfidf](https:\u002F\u002Fgithub.com\u002Fferristseng\u002Frust-tfidf) - 用于计算 TF-IDF 的库。\n- [messense\u002Ffasttext-rs](https:\u002F\u002Fgithub.com\u002Fmessense\u002Ffasttext-rs) - fastText 的 Rust 绑定。\n- [mklf\u002Fword2vec-rs](https:\u002F\u002Fgithub.com\u002Fmklf\u002Fword2vec-rs) - 纯 Rust 实现的 Word2Vec。\n- [DimaKudosh\u002Fword2vec](https:\u002F\u002Fgithub.com\u002FDimaKudosh\u002Fword2vec) - Word2Vec 的 Rust 接口。\n- [lloydmeta\u002Fsloword2vec-rs](https:\u002F\u002Fgithub.com\u002Flloydmeta\u002Fsloword2vec-rs) - 一个朴素（即速度较慢）的 Word2Vec 实现。内部使用 BLAS 加速计算。\n\n\n# 推荐系统\n\n- [PersiaML\u002FPERSIA](https:\u002F\u002Fgithub.com\u002FPersiaML\u002FPERSIA) - 基于 PyTorch 的高性能分布式深度学习推荐系统训练框架。\n- [jackgerrits\u002Fvowpalwabbit-rs](https:\u002F\u002Fgithub.com\u002Fjackgerrits\u002Fvowpalwabbit-rs) - 🦀🐇 Rust 版本的 VowpalWabbit。\n- [outbrain\u002Ffwumious_wabbit](https:\u002F\u002Fgithub.com\u002Foutbrain\u002Ffwumious_wabbit) - Fwumious Wabbit，用 Rust 编写的快速在线机器学习工具包。\n- [hja22\u002Frucommender](https:\u002F\u002Fgithub.com\u002Fhja22\u002Frucommender) - 基于用户的协同过滤算法的 Rust 实现。\n- [maciejkula\u002Fsbr-rs](https:\u002F\u002Fgithub.com\u002Fmaciejkula\u002Fsbr-rs) - 面向 Rust 的深度推荐系统。\n- [chrisvittal\u002Fquackin](https:\u002F\u002Fgithub.com\u002Fchrisvittal\u002Fquackin) - Rust 平台上的推荐系统框架。\n- [snd\u002Fonmf](https:\u002F\u002Fgithub.com\u002Fsnd\u002Fonmf) - 快速的 Rust 实现，在线非负矩阵分解，遵循论文“通过在线非负矩阵分解检测并追踪潜在因子”的方法。\n- [rhysnewell\u002Fnymph](https:\u002F\u002Fgithub.com\u002Frhysnewell\u002Fnymph) - Rust 版本的非负矩阵分解。\n\n\n# 信息检索\n\n## 全文检索\n\n- [quickwit-inc\u002Fquickwit](https:\u002F\u002Fgithub.com\u002Fquickwit-inc\u002Fquickwit) - Quickwit 是一款大数据搜索引擎。\n- [bayard-search\u002Fbayard](https:\u002F\u002Fgithub.com\u002Fbayard-search\u002Fbayard) - 用 Rust 编写的全文搜索与索引服务器。\n- [neuml\u002Ftxtai.rs](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai.rs) - 基于 AI 的 Rust 搜索引擎。\n- [meilisearch\u002FMeiliSearch](https:\u002F\u002Fgithub.com\u002Fmeilisearch\u002FMeiliSearch) - 极速、高度相关且容错性强的搜索引擎。\n- [toshi-search\u002FToshi](https:\u002F\u002Fgithub.com\u002Ftoshi-search\u002FToshi) - 用 Rust 编写的全文搜索引擎。\n- [BurntSushi\u002Ffst](https:\u002F\u002Fgithub.com\u002FBurntSushi\u002Ffst) - 使用有限状态转换器紧凑地表示大型集合和映射。\n- [tantivy-search\u002Ftantivy](https:\u002F\u002Fgithub.com\u002Ftantivy-search\u002Ftantivy) - Tantivy 是受 Apache Lucene 启发、用 Rust 编写的全文搜索引擎库。\n- [tinysearch\u002Ftinysearch](https:\u002F\u002Fgithub.com\u002Ftinysearch\u002Ftinysearch) - 🔍 用 Rust 和 WebAssembly 构建的轻量级静态网站全文搜索引擎。\n- [quantleaf\u002Fprobly-search](https:\u002F\u002Fgithub.com\u002Fquantleaf\u002Fprobly-search) - 一个轻量级的全文搜索库，允许完全控制评分计算。\n- [https:\u002F\u002Fgithub.com\u002Fandylokandy\u002Fsimsearch-rs](https:\u002F\u002Fgithub.com\u002Fandylokandy\u002Fsimsearch-rs) - 一个简单轻量的模糊搜索引擎，可在内存中运行，用于查找相似字符串。\n- [jameslittle230\u002Fstork](https:\u002F\u002Fgithub.com\u002Fjameslittle230\u002Fstork) - 🔎 专为静态网站设计的超高速网页搜索。\n- [elastic\u002Felasticsearch-rs](https:\u002F\u002Fgithub.com\u002Felastic\u002Felasticsearch-rs) - Elasticsearch 的官方 Rust 客户端。\n\n\n## 最近邻搜索\n\n- [Enet4\u002Ffaiss-rs](https:\u002F\u002Fgithub.com\u002FEnet4\u002Ffaiss-rs) - Faiss 的 Rust 语言绑定。\n- [rust-cv\u002Fhnsw](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fhnsw) - HNSW 近似最近邻算法，源自论文“利用分层可导航小世界图高效稳健地进行近似最近邻搜索”。\n- [hora-search\u002Fhora](https:\u002F\u002Fgithub.com\u002Fhora-search\u002Fhora) - 🚀 用 Rust 🦀 实现的高效近似最近邻搜索算法库。horasearch.com。\n- [InstantDomain\u002Finstant-distance](https:\u002F\u002Fgithub.com\u002FInstantDomain\u002Finstant-distance) - 基于 HNSW 索引的 Rust 高速近似最近邻搜索。\n- [lerouxrgd\u002Fngt-rs](https:\u002F\u002Fgithub.com\u002Flerouxrgd\u002Fngt-rs) - NGT 近似最近邻搜索的 Rust 封装。\n- [granne\u002Fgranne](https:\u002F\u002Fgithub.com\u002Fgranne\u002Fgranne) - 基于图的近似最近邻搜索。\n- [u1roh\u002Fkd-tree](https:\u002F\u002Fgithub.com\u002Fu1roh\u002Fkd-tree) - Rust 实现的 k 维树。速度快、简单易用。\n- [qdrant\u002Fqdrant](https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant) - Qdrant 是一款支持扩展过滤功能的向量相似度搜索引擎。\n- [rust-cv\u002Fhwt](https:\u002F\u002Fgithub.com\u002Frust-cv\u002Fhwt) - 来自论文“在汉明空间中进行在线最近邻搜索”的汉明权重树。\n- [fulara\u002Fkdtree-rust](https:\u002F\u002Fgithub.com\u002Ffulara\u002Fkdtree-rust) - Rust 版本的 k-d 树实现。\n- [mrhooray\u002Fkdtree-rs](https:\u002F\u002Fgithub.com\u002Fmrhooray\u002Fkdtree-rs) - 用于快速地理空间索引和查询的 Rust k 维树。\n- [kornelski\u002Fvpsearch](https:\u002F\u002Fgithub.com\u002Fkornelski\u002Fvpsearch) - 用于在集合中查找最近（最相似）元素的 C 库。\n- [petabi\u002Fpetal-neighbors](https:\u002F\u002Fgithub.com\u002Fpetabi\u002Fpetal-neighbors) - 包括球树和视点树在内的最近邻搜索算法。\n- [ritchie46\u002Flsh-rs](https:\u002F\u002Fgithub.com\u002Fritchie46\u002Flsh-rs) - Rust 实现的局部敏感哈希，并带有 Python 绑定。\n- [kampersanda\u002Fmih-rs](https:\u002F\u002Fgithub.com\u002Fkampersanda\u002Fmih-rs) - Rust 实现的多索引哈希算法，用于在汉明空间中对 64 位编码进行近邻搜索。\n\n# 强化学习\n\n- [taku-y\u002Fborder](https:\u002F\u002Fgithub.com\u002Ftaku-y\u002Fborder) - Border 是一个用 Rust 编写的强化学习库。\n- [NivenT\u002FREnforce](https:\u002F\u002Fgithub.com\u002FNivenT\u002FREnforce) - 用 Rust 编写的强化学习库\n- [edlanglois\u002Frelearn](https:\u002F\u002Fgithub.com\u002Fedlanglois\u002Frelearn) - 使用 Rust 进行强化学习\n- [tspooner\u002Frsrl](https:\u002F\u002Fgithub.com\u002Ftspooner\u002Frsrl) - 一个快速、安全且易于使用的 Rust 强化学习框架。\n- [milanboers\u002Frurel](https:\u002F\u002Fgithub.com\u002Fmilanboers\u002Frurel) - 灵活、可重用的 Rust 强化学习（Q 学习）实现\n- [Ragnaroek\u002Fbandit](https:\u002F\u002Fgithub.com\u002FRagnaroek\u002Fbandit) - Rust 中的多臂老虎机算法\n- [MrRobb\u002Fgym-rs](https:\u002F\u002Fgithub.com\u002Fmrrobb\u002Fgym-rs) - Rust 的 OpenAI Gym 绑定\n\n\n# 监督学习模型\n\n- [tomtung\u002Fomikuji](https:\u002F\u002Fgithub.com\u002Ftomtung\u002Fomikuji) - 用于极端多标签分类的分区标签树及其变体的高效实现\n- [shadeMe\u002Fliblinear-rs](https:\u002F\u002Fgithub.com\u002Fshademe\u002Fliblinear-rs) - LIBLINEAR C\u002FC++ 库的 Rust 语言绑定。\n- [messense\u002Fcrfsuite-rs](https:\u002F\u002Fgithub.com\u002Fmessense\u002Fcrfsuite-rs) - crfsuite 的 Rust 绑定\n- [ralfbiedert\u002Fffsvm-rust](https:\u002F\u002Fgithub.com\u002Fralfbiedert\u002Fffsvm-rust) - FFSVM 代表“非常快速的支持向量机”\n- [zenoxygen\u002Fbayespam](https:\u002F\u002Fgithub.com\u002Fzenoxygen\u002Fbayespam) - 用 Rust 编写的简单贝叶斯垃圾邮件分类器。\n- [Rui_Vieira\u002Fnaive-bayesnaive-bayes](https:\u002F\u002Fgitlab.com\u002Fruivieira\u002Fnaive-bayes) - 用 Rust 编写的朴素贝叶斯分类器。\n- [Rui_Vieira\u002Frandom-forests](https:\u002F\u002Fgitlab.com\u002Fruivieira\u002Frandom-forests) - 用于随机森林的 Rust 库。\n- [sile\u002Frandomforest](https:\u002F\u002Fgithub.com\u002Fsile\u002Frandomforest) - Rust 中的随机森林实现\n- [tomtung\u002Fcraftml-rs](https:\u002F\u002Fgithub.com\u002Ftomtung\u002Fcraftml-rs) - CRAFTML 的 Rust 实现，一种高效的基于聚类的随机森林，用于极端多标签学习\n- [nkaush\u002Fnaive-bayes-rs](https:\u002F\u002Fgithub.com\u002Fnkaush\u002Fnaive-bayes-rs) - 一个包含自制机器学习模型的 Rust 库，用于对 MNIST 数据集进行分类。该库的构建旨在熟悉 Rust 的高级概念。\n\n\n# 非监督学习与聚类模型\n\n- [frjnn\u002Fbhtsne](https:\u002F\u002Fgithub.com\u002Ffrjnn\u002Fbhtsne) - 用 Rust 编写的 Barnes-Hut t-SNE 实现。\n- [vaaaaanquish\u002Flabel-propagation-rs](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Flabel-propagation-rs) - 用 Rust 实现的标签传播算法。标签传播（LP）是一种基于图的半监督学习（SSL）。已实现了 LGC 和 CAMLP。\n- [nmandery\u002Fextended-isolation-forest](https:\u002F\u002Fgithub.com\u002Fnmandery\u002Fextended-isolation-forest) - 用于异常检测的扩展孤立森林算法的 Rust 移植\n- [avinashshenoy97\u002FRusticSOM](https:\u002F\u002Fgithub.com\u002Favinashshenoy97\u002FRusticSOM) - 用于自组织映射（SOM）的 Rust 库。\n- [diffeo\u002Fkodama](https:\u002F\u002Fgithub.com\u002Fdiffeo\u002Fkodama) - Rust 中的快速层次聚类\n- [kno10\u002Frust-kmedoids](https:\u002F\u002Fgithub.com\u002Fkno10\u002Frust-kmedoids) - 使用 FasterPAM 算法的 Rust k-Medoids 聚类\n- [petabi\u002Fpetal-clustering](https:\u002F\u002Fgithub.com\u002Fpetabi\u002Fpetal-clustering) - DBSCAN 和 OPTICS 聚类算法。\n- [savish\u002Fdbscan](https:\u002F\u002Fgithub.com\u002Fsavish\u002Fdbscan) - Rust 中的朴素 DBSCAN 实现\n- [gu18168\u002FDBSCANSD](https:\u002F\u002Fgithub.com\u002Fgu18168\u002FDBSCANSD) - DBSCANSD 的 Rust 实现，这是一种轨迹聚类算法。\n- [lazear\u002Fdbscan](https:\u002F\u002Fgithub.com\u002Flazear\u002Fdbscan) - Rust 中无依赖的 DBSCAN 聚类实现\n- [whizsid\u002Fkddbscan-rs](https:\u002F\u002Fgithub.com\u002Fwhizsid\u002Fkddbscan-rs) - 受 kDDBSCAN 聚类算法启发的 Rust 库\n- [Sauro98\u002Fappr_dbscan_rust](https:\u002F\u002Fgithub.com\u002FSauro98\u002Fappr_dbscan_rust) - 实现 Gan 和 Tao 提出的 DBSCAN 近似版本的程序\n- [quietlychris\u002Fdensity_clusters](https:\u002F\u002Fgithub.com\u002Fquietlychris\u002Fdensity_clusters) - 用 Rust 编写的朴素密度聚类算法\n- [milesgranger\u002Fgap_statistic](https:\u002F\u002Fgithub.com\u002Fmilesgranger\u002Fgap_statistic) - 动态获取非监督学习中数据的建议聚类数。\n- [genbattle\u002Frkm](https:\u002F\u002Fgithub.com\u002Fgenbattle\u002Frkm) - 用 Rust 编写的通用 k-means 实现\n- [selforgmap\u002Fsom-rust](https:\u002F\u002Fgithub.com\u002Fselforgmap\u002Fsom-rust) - 自组织映射（SOM）是一种人工神经网络（ANN），通过无监督的竞争性学习训练，以生成高维数据的低维离散表示（特征图）。\n\n\n# 统计模型\n\n- [Redpoll\u002Fchangepoint](https:\u002F\u002Fgitlab.com\u002FRedpoll\u002Fchangepoint) - 包含以下变点检测算法：Bocpd -- 在线贝叶斯变点检测参考。BocpdTruncated -- 与 Bocpd 相同，但在运行长度分布不太可能时将其截断。\n- [krfricke\u002Farima](https:\u002F\u002Fgithub.com\u002Fkrfricke\u002Farima) - 用于 Rust 的 ARIMA 建模\n- [Daingun\u002Fautomatica](https:\u002F\u002Fgitlab.com\u002Fdaingun\u002Fautomatica) - 自动控制系统库\n- [rbagd\u002Frust-linearkalman](https:\u002F\u002Fgithub.com\u002Frbagd\u002Frust-linearkalman) - Rust 中的卡尔曼滤波与平滑\n- [sanity\u002Fpair_adjacent_violators](https:\u002F\u002Fgithub.com\u002Fsanity\u002Fpair_adjacent_violators) - Rust 中等秩回归的相邻违规者算法实现\n\n\n# 进化算法\n\n- [martinus\u002Fdifferential-evolution-rs](https:\u002F\u002Fgithub.com\u002Fmartinus\u002Fdifferential-evolution-rs) - 用于 Rust 的通用差分进化\n- [innoave\u002Fgenevo](https:\u002F\u002Fgithub.com\u002Finnoave\u002Fgenevo) - 以可定制和可扩展的方式执行遗传算法（GA）模拟。\n- [Jeffail\u002Fspiril](https:\u002F\u002Fgithub.com\u002FJeffail\u002Fspiril) - 用于遗传算法的 Rust 库\n- [sotrh\u002Frust-genetic-algorithm](https:\u002F\u002Fgithub.com\u002Fsotrh\u002Frust-genetic-algorithm) - Rust 和 Python 中的遗传算法示例\n- [willi-kappler\u002Fdarwin-rs](https:\u002F\u002Fgithub.com\u002Fwilli-kappler\u002Fdarwin-rs) - darwin-rs，使用 Rust 的进化算法\n\n\n# 参考\n\n## 周边项目\n\n- [Are we learning yet?](http:\u002F\u002Fwww.arewelearningyet.com\u002F)，一项正在进行中的工作，旨在编目 Rust 中机器学习的发展状况\n- [e-tony\u002Fbest-of-ml-rust](https:\u002F\u002Fgithub.com\u002Fe-tony\u002Fbest-of-ml-rust)，一个精选的优秀机器学习 Rust 库列表\n- [最佳 51 个 Rust 机器学习库](https:\u002F\u002Frustrepo.com\u002Fcatalog\u002Frust-machine-learning_newest_1)，RustRepo\n- [rust-unofficial\u002Fawesome-rust](https:\u002F\u002Fgithub.com\u002Frust-unofficial\u002Fawesome-rust)，一个精心整理的 Rust 代码和资源列表\n- [前 16 名 Rust 机器学习项目](https:\u002F\u002Fwww.libhunt.com\u002Fl\u002Frust\u002Ft\u002Fmachine-learning)，被归类为机器学习的开源 Rust 项目\n- [39+ 最佳 Rust 机器学习框架、库、软件和资源](https:\u002F\u002Freposhub.com\u002Frust\u002Fmachine-learning)，ReposHub\n\n\n## 博客\n\n### 引言\n\n- [关于 Rust 的机器学习社区](https:\u002F\u002Fmedium.com\u002F@autumn_eng\u002Fabout-rust-s-machine-learning-community-4cda5ec8a790#.hvkp56j3f)，Medium，2016年1月6日，Autumn Engineering\n- [Rust 与 Python：技术与业务对比](https:\u002F\u002Fwww.ideamotive.co\u002Fblog\u002Frust-vs-python-technology-and-business-comparison)，2021年3月4日，Miłosz Kaczorowski\n- [我编写了最快的 DataFrame 库之一](https:\u002F\u002Fwww.ritchievink.com\u002Fblog\u002F2021\u002F02\u002F28\u002Fi-wrote-one-of-the-fastest-dataframe-libraries)，2021年2月28日，Ritchie Vink \n- [Polars：你从未听说过的最快 DataFrame 库](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2021\u002F06\u002Fpolars-the-fastest-dataframe-library-youve-never-heard-of)，2021年1月19日，Analytics Vidhya \n- [数据操作：Polars 与 Rust 对比](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdata-manipulation-polars-vs-rust--3def44c8)，2021年3月13日，Xavier Tao\n- [Rust 中的机器学习现状——Ehsan 的博客](https:\u002F\u002Fehsanmkermani.com\u002F2019\u002F05\u002F13\u002Fstate-of-machine-learning-in-rust\u002F)，2019年5月13日，由 Ehsan 发布\n- [机器学习工程师 Ritchie Vink 编写了 Polars，这是 Python 和 Rust 中最快的 DataFrame 库之一](https:\u002F\u002Fwww.xomnia.com\u002Fpost\u002Fritchie-vink-writes-polars-one-of-the-fastest-dataframe-libraries-in-python-and-rust\u002F)，Xomnia，2021年5月11日\n- [Quickwit：一款高性价比的 Rust 搜索引擎](https:\u002F\u002Fquickwit.io\u002Fblog\u002Fquickwit-first-release\u002F)，2021年7月13日，quickwit，PAUL MASUREL\n- [数据操作：Polars 与 Rust 对比](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdata-manipulation-polars-vs-rust--3def44c8)，2021年3月13日，Xavier Tao\n- [看看 Rust 在生产环境中的应用](https:\u002F\u002Fserokell.io\u002Fblog\u002Frust-in-production-qovery)，2021年8月10日，Qovery，@serokell\n- [为什么我选择 Rust 而不是继续使用 Python](https:\u002F\u002Fmedium.com\u002Fgeekculture\u002Fwhy-i-started-rust-instead-of-stick-to-python-626bab07479a)，2021年9月26日，Medium，Geek Culture，Marshal SHI\n\n\n### 教程\n\n- [Rust 机器学习书籍](https:\u002F\u002Frust-ml.github.io\u002Fbook\u002Fchapter_1.html)，包含使用 linfa-clustering 的 KMeans 和 DBSCAN 示例\n- [人工智能与机器学习——实用 Rust 项目：构建游戏、物理计算和机器学习应用——Dev Guis](http:\u002F\u002Fdevguis.com\u002F6-artificial-intelligence-and-machine-learning-practical-rust-projects-building-game-physical-computing-and-machine-learning-applications.html)，2021年5月19日\n- [使用 Linfa 在 Rust 中进行机器学习](https:\u002F\u002Fblog.logrocket.com\u002Fmachine-learning-in-rust-using-linfa\u002F)，LogRocket 博客，2021年4月30日，Timeular，Mario Zupan，包含 LogisticRegression 示例\n- [Rust 中的机器学习，Smartcore](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-smartcore-2f472d1ce83)，Medium，The Startup，2021年1月15日，[Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F)，包含 LinerRegression、Random Forest Regressor 和 K-Fold 示例\n- [Rust 中的机器学习，逻辑回归](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-logistic-regression-74d6743df161)，Medium，The Startup，2021年1月6日，[Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F)\n- [Rust 中的机器学习，线性回归](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fmachine-learning-in-rust-linear-regression-edef3fb65f93)，Medium，The Startup，2020年12月16日，[Vlad Orlov](https:\u002F\u002Fvolodymyr-orlov.medium.com\u002F)\n- [Rust 中的机器学习](https:\u002F\u002Fathemathmo.github.io\u002F2016\u002F03\u002F07\u002Frusty-machine.html)，2016年3月7日，James，包含 LogisticRegressor 示例\n- [机器学习与 Rust（第 1 部分）：入门！](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-1-getting-started-745885771bc2)，Level Up Coding，2021年1月9日，Stefano Bosisio \n- [机器学习与 Rust（第 2 部分）：线性回归](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-2-linear-regression-d3b820ed28f9)，Level Up Coding，2021年6月15日，Stefano Bosisio \n- [机器学习与 Rust（第 3 部分）：Smartcore、DataFrame 和线性回归](https:\u002F\u002Flevelup.gitconnected.com\u002Fmachine-learning-and-rust-part-3-smartcore-dataframe-and-linear-regression-10451fdc2e60)，Level Up Coding，2021年7月1日，Stefano Bosisio \n- [TensorFlow Rust 实战 第 1 部分](https:\u002F\u002Fwww.programmersought.com\u002Farticle\u002F18696273900\u002F)，程序员之求，2018年\n- [ndarray 的机器学习简介](https:\u002F\u002Fbarcelona.rustfest.eu\u002Fsessions\u002Fmachine-learning-ndarray)，RustFest 2019，2019年11月12日，[Luca Palmieri](https:\u002F\u002Fgithub.com\u002FLukeMathWalker)\n- [用 Rust 从零开始实现简单线性回归](https:\u002F\u002Fcheesyprogrammer.com\u002F2018\u002F12\u002F13\u002Fsimple-linear-regression-from-scratch-in-rust\u002F)，Web 开发、软件架构、算法等领域，2018年12月13日，philipp\n- [使用 EVCXR 在 REPL 和 Jupyter Notebook 中进行交互式 Rust 编程](https:\u002F\u002Fdepth-first.com\u002Farticles\u002F2020\u002F09\u002F21\u002Finteractive-rust-in-a-repl-and-jupyter-notebook-with-evcxr\u002F)，Depth-First，2020年9月21日，Richard L. Apodaca\n- [面向数据科学的 Rust：教程 1](https:\u002F\u002Fdev.to\u002Fdavidedelpapa\u002Frust-for-data-science-tutorial-1-4g5j)，dev，2021年8月25日，Davide Del Papa\n- [petgraph_review](https:\u002F\u002Ftimothy.hobbs.cz\u002Frust-play\u002Fpetgraph_review.html)，2019年10月11日，Timothy Hobbs\n- [用于机器学习的 Rust。Rust](https:\u002F\u002Fmedium.com\u002Ftempus-ex\u002Frust-for-ml-fba0421b0959)，Medium，Tempus Ex，2021年8月1日，Michael Naquin\n- [使用 Rust 和机器学习进行无人机摄影测量的冒险之旅（使用 linfa 和 DBSCAN 进行图像分割）](http:\u002F\u002Fcmoran.xyz\u002Fwriting\u002Fadventures_in_photogrammetry)，2021年11月14日，CHRISTOPHER MORAN\n\n### 应用\n\n- [Rust中的深度学习：迈出第一步](https:\u002F\u002Fmedium.com\u002F@tedsta\u002Fdeep-learning-in-rust-7e228107cccc)，Medium，2016年2月2日，Theodore DeRego\n- [Rust版SentencePiece实现](https:\u002F\u002Fguillaume-be.github.io\u002F2020-05-30\u002Fsentence_piece)，Rust NLP tales，2020年5月30日\n- [使用Rust加速文本生成](https:\u002F\u002Fguillaume-be.github.io\u002F2020-11-21\u002Fgeneration_benchmarks)，Rust NLP tales，2020年11月21日\n- [用Rust编写的简单文本摘要器](https:\u002F\u002Ftowardsdatascience.com\u002Fa-simple-text-summarizer-written-in-rust-4df05f9327a5)，Towards Data Science，2020年11月24日，[Charles Chan](https:\u002F\u002Fchancharles.medium.com\u002F)，文本句子向量、余弦距离和PageRank示例\n- [在Rust中提取深度学习图像嵌入](https:\u002F\u002Flogicai.io\u002Fblog\u002Fextracting-image-embeddings\u002F)，RecoAI，2021年6月1日，Paweł Jankiewic，ONNX示例\n- [使用GPU的Rust深度学习](https:\u002F\u002Fable.bio\u002FhaixuanTao\u002Fdeep-learning-in-rust-with-gpu--26c53a7f)，2021年7月30日，Xavier Tao\n- [tch-rs预训练示例 - 用于PyTorch Rust绑定tch-rs的Docker。预训练模型示例](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Ftch-rs-pretrain-example-docker)，2021年8月15日，vaaaaanquish\n- [Rust ANN搜索示例 - 使用近似最近邻库在Rust中进行图像搜索的示例](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Frust-ann-search-example)，2021年8月15日，vaaaaanquish\n- [dzamkov\u002Fdeep-learning-test - 仅使用线性代数库（nalgebra）在Rust中实现深度学习](https:\u002F\u002Fgithub.com\u002Fdzamkov\u002Fdeep-learning-test)，2021年8月30日，dzamkov\n- [vaaaaanquish\u002Frust-machine-learning-api-example - 使用resnet224对以base64格式接收的图像进行推理并返回结果的axum示例](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002Frust-machine-learning-api-example)，2021年9月7日，vaaaaanquish\n- [Rust用于机器学习：一次性性能基准测试 - 用于一次性图像识别的暹罗神经网络的Rust实现，用于基准测试性能和结果](https:\u002F\u002Futmist.gitlab.io\u002Fprojects\u002Frust-ml-oneshot\u002F)，多伦多大学机器智能学生团队\n- [为什么Wallaroo从Pony迁移到Rust](https:\u002F\u002Fwallarooai.medium.com\u002Fwhy-wallaroo-moved-from-pony-to-rust-292e7339fc34)，2021年8月19日，Wallaroo.ai\n- [epwalsh\u002Frust-dl-webserver - 使用批处理预测在Rust中服务深度学习模型的示例](https:\u002F\u002Fgithub.com\u002Fepwalsh\u002Frust-dl-webserver)，2021年11月16日，epwalsh\n\n\n### 案例研究\n\n- [生产环境用户 - Rust编程语言](https:\u002F\u002Fwww.rust-lang.org\u002Fproduction\u002Fusers)，由rust-lang.org提供\n- [使用Rust将机器学习投入生产：速度提升25倍](https:\u002F\u002Fwww.lpalmieri.com\u002Fposts\u002F2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup\u002F)，A LEARNING JOURNAL，2019年12月1日，[@algo_luca](https:\u002F\u002Ftwitter.com\u002Falgo_luca)\n- [9家在生产环境中使用Rust的公司](https:\u002F\u002Fserokell.io\u002Fblog\u002Frust-companies)，serokell，2020年11月18日，Gints Dreimanis\n- [Wasm上的掩码语言模型，前端BERT示例](https:\u002F\u002Fgithub.com\u002Foptim-corp\u002Fmasked-lm-wasm\u002F)，optim-corp\u002Fmasked-lm-wasm，2021年8月27日，Optim\n- [使用Actix-Web服务TensorFlow](https:\u002F\u002Fgithub.com\u002Fkykosic\u002Factix-tensorflow-example)，kykosic\u002Factix-tensorflow-example\n- [使用Actix-Web服务PyTorch](https:\u002F\u002Fgithub.com\u002Fkykosic\u002Factix-pytorch-example)，kykosic\u002Factix-pytorch-example\n\n\n## 讨论\n\n- [Rust中的自然语言处理：rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002F5jj8vr\u002Fnatural_language_processing_in_rust\u002F)，2016年12月6日\n- [Rust编程语言中机器学习的未来前景：MachineLearning](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002F7iz51p\u002Fd_future_prospect_of_machine_learning_in_rust\u002F)，2017年11月11日\n- [对Rust中NLP的兴趣？ - Rust编程语言论坛](https:\u002F\u002Fusers.rust-lang.org\u002Ft\u002Finterest-for-nlp-in-rust\u002F15331)，2018年1月19日\n- [Rust适合深度学习和人工智能吗？ - Rust编程语言论坛](https:\u002F\u002Fusers.rust-lang.org\u002Ft\u002Fis-rust-good-for-deep-learning-and-artificial-intelligence\u002F22866)，2018年11月18日\n- [ndarray与nalgebra：rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fbtn1cz\u002Fndarray_vs_nalgebra\u002F)，2019年5月28日\n- [使用Rust将ML投入生产 | Hacker News](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=21680965)，2019年12月2日\n- [谁在生产\u002F研究中使用Rust进行机器学习？：rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Ffvehyq\u002Fd_who_is_using_rust_for_machine_learning_in\u002F)，2020年4月5日\n- [Rust中的深度学习](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Figz8iv\u002Fdeep_learning_in_rust\u002F)，2020年8月26日\n- [SmartCore，适用于Rust的快速且全面的机器学习库！：rust](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fj1mj1g\u002Fsmartcore_fast_and_comprehensive_machine_learning\u002F)，2020年9月29日\n- [使用ONNX的GPU版Rust深度学习](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002Fouul33\u002Fd_p_deep_learning_in_rust_with_gpu_on_onnx\u002F)，2021年7月31日\n- [Rust与C++：这些流行编程语言之间的主要区别](https:\u002F\u002Fcodilime.com\u002Fblog\u002Frust-vs-cpp-the-main-differences-between-these-popular-programming-languages\u002F)，2021年8月25日\n- [我想分享作为深度学习研究人员使用Rust的经验](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fpft9n9\u002Fi_wanted_to_share_my_experience_of_rust_as_a_deep\u002F)，2021年9月2日\n- [Rust的ML生态系统目前发展到什么程度了？](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Frust\u002Fcomments\u002Fpoglgg\u002Fhow_far_along_is_the_ml_ecosystem_with_rust\u002F)，2021年9月15日\n\n\n## 图书\n\n- [使用Rust进行实用机器学习：用Rust创建智能应用（英文版）](https:\u002F\u002Famzn.to\u002F3h7JV8U)，2019年12月10日，Joydeep Bhattacharjee\n    - 用Rust编写机器学习算法\n    - 使用Rust库完成机器学习中的不同任务\n    - 为你的机器学习应用创建简洁的Rust包\n    - 在Rust中实现NLP和计算机视觉\n    - 将代码部署到云端和裸机服务器上\n    - 源代码：[Apress\u002Fpractical-machine-learning-w-rust](https:\u002F\u002Fgithub.com\u002FApress\u002Fpractical-machine-learning-w-rust)\n- [使用Rust笔记本进行数据分析](https:\u002F\u002Fdatacrayon.com\u002Fshop\u002Fproduct\u002Fdata-analysis-with-rust-notebooks\u002F)，2021年9月3日，Shahin Rostami\n    - 使用Plotters和Plotly进行绘图\n    - 使用ndarray进行操作\n    - 描述性统计\n    - 交互式图表\n    - 共现类型的可视化\n    - 下载源代码和数据集\n    - 完整文本：\n        - [https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F](https:\u002F\u002Fdatacrayon.com\u002Fposts\u002Fprogramming\u002Frust-notebooks\u002Fpreface\u002F)\n\n## 影片\n\n- [The \u002Fr\u002Fplayrust 分类器：现实世界中的 Rust 数据科学](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lY10kTcM8ek)，RustConf 2016，2016年10月5日，Suchin Gururangan 和 Colin O'Brien\n- [在 Rust 中构建 AI 单元](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UHFlKAmANJg)，FOSSASIA 2018，2018年3月25日，Vigneshwer Dhinakaran\n- [Python 与 Rust 在仿真中的对比](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kytvDxxedWY)，EuroPython 2019，2019年7月10日，Alisa Dammer\n- [机器学习正在变革——Rust 是合适的工具吗？](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=odI_LY8AIqo)，RustLab 2019，2019年10月31日，Luca Palmieri\n- [在嵌入式 Rust 中使用 TensorFlow](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DUVE86yTfKU)，2020年9月29日，Ferrous Systems GmbH，Richard Meadows\n- [用 Rust 编写最快的 GBDT 库](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=D1NAREuicNs)，2021年9月16日，RustConf 2021，Isabella Tromba\n\n\n## 播客\n\n- DATA SCIENCE AT HOME\n    - [Rust 与机器学习 #1（第107集）](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-1-ep-107\u002F)\n    - [Rust 与机器学习 #2 与 Luca Palmieri（第108集）](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-2-with-luca-palmieri-ep-108\u002F)\n    - [Rust 与机器学习 #3 与 Alec Mocatta（第109集）](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-3-with-alec-mocatta-ep-109\u002F)\n    - [Rust 与机器学习 #4：实用工具（第110集）](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-machine-learning-4-practical-tools-ep-110\u002F)\n    - [Rust 中的机器学习：与 Alec Mocatta 的 Amadeus 对谈（第127集）](https:\u002F\u002Fdatascienceathome.com\u002Fmachine-learning-in-rust-amadeus-with-alec-mocatta-rb-ep-127\u002F)\n    - [Rust 与深度学习：与 Daniel McKenna 对谈（第135集）](https:\u002F\u002Fdatascienceathome.com\u002Frust-and-deep-learning\u002F)\n    - [Rust 是否足够灵活以支持灵活的数据模型？（第137集）](https:\u002F\u002Fdatascienceathome.com\u002Fis-rust-flexible-enough-for-a-flexible-data-model-ep-137\u002F)\n    - [Pandas 与 Rust 对比（第144集）](https:\u002F\u002Fdatascienceathome.com\u002Fpandas-vs-rust-ep-144\u002F)\n    - [Apache Arrow、Ballista 与大数据在 Rust 中的应用：与 Andy Grove 对谈（第145集）](https:\u002F\u002Fdatascienceathome.com\u002Fapache-arrow-ballista-and-big-data-in-rust-with-andy-grove-ep-145\u002F)\n    - [Polars：Rust 中最快的 DataFrame crate（第146集）](https:\u002F\u002Fdatascienceathome.com\u002Fpolars-the-fastest-dataframe-crate-in-rust-ep-146\u002F)\n    - [Apache Arrow、Ballista 与大数据在 Rust 中的应用：与 Andy Grove 对谈 RB 版本（第160集）](https:\u002F\u002Fdatascienceathome.com\u002Fapache-arrow-ballista-and-big-data-in-rust-with-andy-grove-rb-ep-160\u002F)\n\n\n## 论文\n\n- [Rust 中的端到端 NLP 流程](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.nlposs-1.4.pdf)，第二届 NLP 开源软件研讨会（NLP-OSS）论文集，第20–25页，虚拟会议，2020年11月19日，Guillaume Becquin\n\n\n# 如何贡献\n\n请直接更新 README.md 文件。\n\n一旦您更新了此 README.md 文件，CI 将会自动执行，\n同时网站内容也会相应更新。\n\n\n# 感谢\n\n感谢所有相关项目。\n\n[https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning](https:\u002F\u002Fgithub.com\u002Fvaaaaanquish\u002FAwesome-Rust-MachineLearning)","# Awesome-Rust-MachineLearning 快速上手指南\n\n本指南旨在帮助开发者快速利用 Rust 生态中的机器学习资源，特别适合希望从 Python 迁移到 Rust 的用户。`Awesome-Rust-MachineLearning` 本身是一个 curated list（精选列表），而非单一库，因此“上手”意味着配置好 Rust 开发环境并掌握核心工具链的使用。\n\n## 环境准备\n\n在开始之前，请确保你的系统满足以下要求：\n\n*   **操作系统**：Linux, macOS, 或 Windows (WSL2 推荐)。\n*   **Rust 工具链**：需安装最新的稳定版 Rust。\n*   **前置依赖**：\n    *   `git`：用于克隆仓库和获取源码。\n    *   `C++ 编译器` (如 `gcc`, `clang`, 或 MSVC)：部分底层数学库（如 `ndarray`, `nalgebra` 的某些后端）或 OpenCV 绑定可能需要编译原生代码。\n    *   `Jupyter` (可选)：如果你打算使用交互式笔记本进行探索，需预先安装 Python 版的 Jupyter Notebook 或 JupyterLab。\n\n### 安装 Rust\n官方推荐使用 `rustup` 进行管理。国内开发者若遇到连接超时，可使用国内镜像源加速安装：\n\n```bash\n# 使用国内镜像源安装 rustup\nexport RUSTUP_DIST_SERVER=https:\u002F\u002Fmirrors.ustc.edu.cn\u002Frust-static\nexport RUSTUP_UPDATE_ROOT=https:\u002F\u002Fmirrors.ustc.edu.cn\u002Frust-static\u002Frustup\ncurl --proto '=https' --tlsv1.2 -sSf https:\u002F\u002Fmirrors.ustc.edu.cn\u002Frust-static\u002Frustup-init.sh | sh\n```\n\n安装完成后，验证版本：\n```bash\nrustc --version\ncargo --version\n```\n\n## 安装步骤\n\n由于这是一个资源列表，你不需要“安装”整个项目。你需要根据需求，在你的项目中引入具体的 crate（库）。\n\n### 1. 创建新项目\n```bash\ncargo new ml_project\ncd ml_project\n```\n\n### 2. 添加核心依赖\n根据该列表推荐，以下是机器学习最常用的基础库。编辑 `Cargo.toml` 添加依赖：\n\n```toml\n[dependencies]\n# 多维数组与矩阵运算 (类似 NumPy)\nndarray = \"0.15\"\nndarray-linalg = \"0.16\" # 线性代数后端\n\n# 数据处理与 DataFrame (类似 Pandas，强烈推荐)\npolars = \"0.38\" \n\n# 绘图库 (类似 Matplotlib)\nplotters = \"0.3\"\n\n# 深度学习框架 (示例：Burn 或 Candle，视具体需求而定)\n# burn = \"0.13\" \n```\n\n*注：版本号请以 crates.io 最新为准。*\n\n### 3. 配置交互式环境 (可选但推荐)\n为了获得类似 Python Jupyter 的体验，推荐安装 `evcxr` Jupyter 内核。\n\n```bash\n# 安装 evcxr_jupyter\ncargo install evcxr_jupyter\n\n# 注册内核到 Jupyter\nevcxr_jupyter --install\n```\n\n安装完成后，启动 `jupyter notebook`，新建文件时即可选择 \"Rust\" 内核。\n\n## 基本使用\n\n以下示例展示如何结合 `polars` (数据处理) 和 `ndarray` (数值计算) 完成一个简单的数据加载与矩阵运算流程。\n\n### 示例：数据加载与基础运算\n\n创建一个 `src\u002Fmain.rs` 文件：\n\n```rust\nuse ndarray::{arr2, Array2};\nuse polars::prelude::*;\n\nfn main() -> PolarsResult\u003C()> {\n    \u002F\u002F 1. 使用 Polars 创建 DataFrame (类似 Pandas)\n    let s1 = Series::new(\"feature_a\", &[1.0, 2.0, 3.0, 4.0]);\n    let s2 = Series::new(\"feature_b\", &[5.0, 6.0, 7.0, 8.0]);\n    let df = DataFrame::new(vec![s1, s2])?;\n\n    println!(\"原始数据:\\n{}\", df);\n\n    \u002F\u002F 2. 简单数据过滤\n    let filtered = df.filter(&df.column(\"feature_a\")?.gt(2.0)?)?;\n    println!(\"\\n过滤后 (feature_a > 2.0):\\n{}\", filtered);\n\n    \u002F\u002F 3. 转换为 ndarray 进行数值计算 (模拟模型输入)\n    \u002F\u002F 注意：实际生产中建议使用 polars 直接导出或专用桥接库\n    let data_vec: Vec\u003Cf64> = filtered.column(\"feature_b\")?.f64()?.into_no_null_iter().collect();\n    let matrix: Array2\u003Cf64> = arr2(&[data_vec.as_slice(), data_vec.as_slice()]).t().to_owned();\n\n    println!(\"\\nNdarray Matrix Shape: {:?}\", matrix.dim());\n    println!(\"Matrix Content:\\n{}\", matrix);\n\n    Ok(())\n}\n```\n\n运行项目：\n```bash\ncargo run\n```\n\n### 示例：在 Jupyter 中绘图 (交互式)\n\n如果你在安装了 `evcxr_jupyter` 的笔记本环境中，可以直接运行以下代码绘制图表：\n\n```rust\n\u002F\u002F 在 Cargo.toml 中需添加: plotters = \"0.3\"\nuse plotters::prelude::*;\n\nlet root = BitMapBackend::new(\"plot.png\", (640, 480)).into_drawing_area();\nroot.fill(&WHITE).unwrap();\n\nlet mut chart = ChartBuilder::on(&root)\n    .caption(\"Rust ML Demo\", (\"sans-serif\", 50).into_font())\n    .margin(5)\n    .x_label_area_size(30)\n    .y_label_area_size(30)\n    .build_cartesian_2d(0f32..10f32, 0f32..10f32)\n    .unwrap();\n\nchart.configure_mesh().draw().unwrap();\n\nchart.draw_series(LineSeries::new(\n    vec![(0.0, 0.0), (5.0, 5.0), (8.0, 7.0)],\n    &RED,\n)).unwrap();\n\nroot.present().unwrap();\n\u002F\u002F 在 Jupyter 中会自动显示图片，本地运行会生成 plot.png\n```\n\n通过以上步骤，你已搭建起基于 Rust 的机器学习开发基础环境，并可参考 `Awesome-Rust-MachineLearning` 仓库中的分类列表（如深度学习、NLP、强化学习等）寻找更专业的算法库进行深入开发。","某金融科技团队正试图将核心风控模型从 Python 迁移至 Rust，以利用其内存安全特性降低生产环境延迟并减少运维成本。\n\n### 没有 Awesome-Rust-MachineLearning 时\n- **选型迷茫**：开发者在 GitHub 上盲目搜索，难以区分哪些机器学习库是活跃维护的，哪些已被废弃，极易踩坑。\n- **生态割裂**：缺乏统一指引，找不到与 Rust 原生数据结构（如 DataFrame、Vector）完美配套的预处理和绘图工具，导致数据流水线断裂。\n- **迁移成本高**：从 Python 转来的工程师不熟悉 Rust 生态，无法快速找到类似 Scikit-learn 的综合库或对应的最佳实践代码注释。\n- **性能优化困难**：不清楚哪些库支持 GPU 加速或针对特定算法（如梯度提升、NLP）有高性能实现，只能重复造轮子。\n\n### 使用 Awesome-Rust-MachineLearning 后\n- **精准选型**：直接查阅分类清晰的清单，快速锁定如 `linfa` 等高质量、持续维护的核心库，避开过时项目。\n- **全链路打通**：依据\"Support Tools\"章节，迅速整合 Jupyter 内核、数据处理及可视化组件，构建完整的 Rust 机器学习工作流。\n- **平滑过渡**：参考针对 Python 迁移者的特别标注和代码评论，团队能迅速理解库的用法，大幅缩短学习曲线。\n- **性能最大化**：通过\"GPU\"和\"Deep Neural Network\"等分类，直接采用经过验证的高性能方案，确保风控系统低延迟运行。\n\nAwesome-Rust-MachineLearning 充当了 Rust 机器学习生态的“导航图”，让团队从混乱的探索者变为高效的构建者，显著加速了高性能 AI 系统的落地。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fvaaaaanquish_Awesome-Rust-MachineLearning_e4d1c743.png","vaaaaanquish","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fvaaaaanquish_5d8aff26.png","Darth Vader","M3, inc.","Tokyo, Japan",null,"https:\u002F\u002Fvaaaaanquish.jp","https:\u002F\u002Fgithub.com\u002Fvaaaaanquish",[82,86],{"name":83,"color":84,"percentage":85},"JavaScript","#f1e05a",50.5,{"name":87,"color":88,"percentage":89},"HTML","#e34c26",49.5,2247,123,"2026-04-07T06:09:28","MIT",4,"","未说明",{"notes":98,"python":99,"dependencies":100},"这是一个 Rust 机器学习库的精选列表（Awesome List），而非单一的可执行软件或框架。因此没有统一的运行环境需求。用户需根据列表中具体选择的某个库（如 polars, tch-rs, burn 等）查阅其各自的文档以获取具体的系统、GPU 及依赖要求。主要开发环境需安装 Rust 工具链。","不适用 (该项目为 Rust 库集合，非 Python 项目)",[101,102,103,104,105],"Rust 工具链","evcxr (可选，用于 Jupyter)","ndarray 或 nalgebra (线性代数)","polars (数据处理)","image-rs (图像处理)",[14,35,15],[108,109,110,111,112,113,114,115],"rust","machine-learning","machine-learning-library","rust-library","natural-language-processing","image-processing","deep-learning","awasome","2026-03-27T02:49:30.150509","2026-04-08T01:07:24.567259",[],[]]