All sites
Data API
Github Data API
Structured JSON data, on demand, with anti-bot bypass built in.
8endpointsRate Limit handled for you
Example (captured live) Open playground
github.com
ExampleJSON responseclick the page to trace a field
{
"query": "machine learning",
"total": 1253090,
"results": [
{
"full_name": "tensorflow/tensorflow",
"name": "tensorflow",
"owner": "tensorflow",
"description": "An Open Source Machine Learning Framework for Everyone",
"url": "https://github.com/tensorflow/tensorflow",
"stars": 199341,
"forks": 76296,
"open_issues": 3196,
"watchers": 199341,
"language": "C++",
"topics": [
"deep-learning",
"deep-neural-networks",
"distributed",
"machine-learning",
"ml",
"neural-network",
"python",
"tensorflow"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2015-11-07T01:19:20Z",
"updated_at": "2026-09-08T16:27:16Z",
"pushed_at": "2026-09-08T17:08:38Z",
"homepage": "https://tensorflow.org",
"default_branch": "master",
"size_kb": 1379097,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "huggingface/transformers",
"name": "transformers",
"owner": "huggingface",
"description": "🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. ",
"url": "https://github.com/huggingface/transformers",
"stars": 164993,
"forks": 34483,
"open_issues": 2402,
"watchers": 164993,
"language": "Python",
"topics": [
"audio",
"deep-learning",
"deepseek",
"gemma",
"glm",
"hacktoberfest",
"llm",
"machine-learning",
"model-hub",
"natural-language-processing",
"nlp",
"pretrained-models",
"python",
"pytorch",
"pytorch-transformers",
"qwen",
"speech-recognition",
"transformer",
"vlm"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2018-10-29T13:56:00Z",
"updated_at": "2026-09-08T15:59:40Z",
"pushed_at": "2026-09-08T17:09:43Z",
"homepage": "https://huggingface.co/transformers",
"default_branch": "main",
"size_kb": 512109,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "microsoft/ML-For-Beginners",
"name": "ML-For-Beginners",
"owner": "microsoft",
"description": "12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all",
"url": "https://github.com/microsoft/ML-For-Beginners",
"stars": 90270,
"forks": 22218,
"open_issues": 16,
"watchers": 90270,
"language": "Jupyter Notebook",
"topics": [
"data-science",
"education",
"machine-learning",
"machine-learning-algorithms",
"machinelearning",
"machinelearning-python",
"microsoft-for-beginners",
"ml",
"python",
"r",
"scikit-learn",
"scikit-learn-python"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2021-03-03T01:34:05Z",
"updated_at": "2026-09-08T16:49:19Z",
"pushed_at": "2026-09-07T00:37:24Z",
"homepage": null,
"default_branch": "main",
"size_kb": 2273724,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "fighting41love/funNLP",
"name": "funNLP",
"owner": "fighting41love",
"description": "中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报告、自然语言生成、NLU太难了系列、自动对联数据及机器人、用户名黑名单列表、罪名法务名词及分类模型、微信公众号语料、cs224n深度学习自然语言处理课程、中文手写汉字识别、中文自然语言处理 语料/数据集、变量命名神器、分词语料库+代码、任务型对话英文数据集、ASR 语音数据集 + 基于深度学习的中文语音识别系统、笑声检测器、Microsoft多语言数字/单位/如日期时间识别包、中华新华字典数据库及api(包括常用歇后语、成语、词语和汉字)、文档图谱自动生成、SpaCy 中文模型、Common Voice语音识别数据集新版、神经网络关系抽取、基于bert的命名实体识别、关键词(Keyphrase)抽取包pke、基于医疗领域知识图谱的问答系统、基于依存句法与语义角色标注的事件三元组抽取、依存句法分析4万句高质量标注数据、cnocr：用来做中文OCR的Python3包、中文人物关系知识图谱项目、中文nlp竞赛项目及代码汇总、中文字符数据、speech-aligner: 从“人声语音”及其“语言文本”产生音素级别时间对齐标注的工具、AmpliGraph: 知识图谱表示学习(Python)库：知识图谱概念链接预测、Scattertext 文本可视化(python)、语言/知识表示工具：BERT & ERNIE、中文对比英文自然语言处理NLP的区别综述、Synonyms中文近义词工具包、HarvestText领域自适应文本挖掘工具（新词发现-情感分析-实体链接等）、word2word：(Python)方便易用的多语言词-词对集：62种语言/3,564个多语言对、语音识别语料生成工具：从具有音频/字幕的在线视频创建自动语音识别(ASR)语料库、构建医疗实体识别的模型（包含词典和语料标注）、单文档非监督的关键词抽取、Kashgari中使用gpt-2语言模型、开源的金融投资数据提取工具、文本自动摘要库TextTeaser: 仅支持英文、人民日报语料处理工具集、一些关于自然语言的基本模型、基于14W歌曲知识库的问答尝试--功能包括歌词接龙and已知歌词找歌曲以及歌曲歌手歌词三角关系的问答、基于Siamese bilstm模型的相似句子判定模型并提供训练数据集和测试数据集、用Transformer编解码模型实现的根据Hacker News文章标题自动生成评论、用BERT进行序列标记和文本分类的模板代码、LitBank：NLP数据集——支持自然语言处理和计算人文学科任务的100部带标记英文小说语料、百度开源的基准信息抽取系统、虚假新闻数据集、Facebook: LAMA语言模型分析，提供Transformer-XL/BERT/ELMo/GPT预训练语言模型的统一访问接口、CommonsenseQA：面向常识的英文QA挑战、中文知识图谱资料、数据及工具、各大公司内部里大牛分享的技术文档 PDF 或者 PPT、自然语言生成SQL语句（英文）、中文NLP数据增强（EDA）工具、英文NLP数据增强工具 、基于医药知识图谱的智能问答系统、京东商品知识图谱、基于mongodb存储的军事领域知识图谱问答项目、基于远监督的中文关系抽取、语音情感分析、中文ULMFiT-情感分析-文本分类-语料及模型、一个拍照做题程序、世界各国大规模人名库、一个利用有趣中文语料库 qingyun 训练出来的中文聊天机器人、中文聊天机器人seqGAN、省市区镇行政区划数据带拼音标注、教育行业新闻语料库包含自动文摘功能、开放了对话机器人-知识图谱-语义理解-自然语言处理工具及数据、中文知识图谱：基于百度百科中文页面-抽取三元组信息-构建中文知识图谱、masr: 中文语音识别-提供预训练模型-高识别率、Python音频数据增广库、中文全词覆盖BERT及两份阅读理解数据、ConvLab：开源多域端到端对话系统平台、中文自然语言处理数据集、基于最新版本rasa搭建的对话系统、基于TensorFlow和BERT的管道式实体及关系抽取、一个小型的证券知识图谱/知识库、复盘所有NLP比赛的TOP方案、OpenCLaP：多领域开源中文预训练语言模型仓库、UER：基于不同语料+编码器+目标任务的中文预训练模型仓库、中文自然语言处理向量合集、基于金融-司法领域(兼有闲聊性质)的聊天机器人、g2pC：基于上下文的汉语读音自动标记模块、Zincbase 知识图谱构建工具包、诗歌质量评价/细粒度情感诗歌语料库、快速转化「中文数字」和「阿拉伯数字」、百度知道问答语料库、基于知识图谱的问答系统、jieba_fast 加速版的jieba、正则表达式教程、中文阅读理解数据集、基于BERT等最新语言模型的抽取式摘要提取、Python利用深度学习进行文本摘要的综合指南、知识图谱深度学习相关资料整理、维基大规模平行文本语料、StanfordNLP 0.2.0：纯Python版自然语言处理包、NeuralNLP-NeuralClassifier：腾讯开源深度学习文本分类工具、端到端的封闭域对话系统、中文命名实体识别：NeuroNER vs. BertNER、新闻事件线索抽取、2019年百度的三元组抽取比赛：“科学空间队”源码、基于依存句法的开放域文本知识三元组抽取和知识库构建、中文的GPT2训练代码、ML-NLP - 机器学习(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM：Facebook的跨语言预训练语言模型、用基于BERT的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取、中文自然语言处理相关的开放任务-数据集-当前最佳结果、CoupletAI - 基于CNN+Bi-LSTM+Attention 的自动对对联系统、抽象知识图谱、MiningZhiDaoQACorpus - 580万百度知道问答数据挖掘项目、brat rapid annotation tool: 序列标注工具、大规模中文知识图谱数据：1.4亿实体、数据增强在机器翻译及其他nlp任务中的应用及效果、allennlp阅读理解:支持多种数据和模型、PDF表格数据提取工具 、 Graphbrain：AI开源软件库和科研工具，目的是促进自动意义提取和文本理解以及知识的探索和推断、简历自动筛选系统、基于命名实体识别的简历自动摘要、中文语言理解测评基准，包括代表性的数据集&基准模型&语料库&排行榜、树洞 OCR 文字识别 、从包含表格的扫描图片中识别表格和文字、语声迁移、Python口语自然语言处理工具集(英文)、 similarity：相似度计算工具包，java编写、海量中文预训练ALBERT模型 、Transformers 2.0 、基于大规模音频数据集Audioset的音频增强 、Poplar：网页版自然语言标注工具、图片文字去除，可用于漫画翻译 、186种语言的数字叫法库、Amazon发布基于知识的人-人开放领域对话数据集 、中文文本纠错模块代码、繁简体转换 、 Python实现的多种文本可读性评价指标、类似于人名/地名/组织机构名的命名体识别数据集 、东南大学《知识图谱》研究生课程(资料)、. 英文拼写检查库 、 wwsearch是企业微信后台自研的全文检索引擎、CHAMELEON：深度学习新闻推荐系统元架构 、 8篇论文梳理BERT相关模型进展与反思、DocSearch：免费文档搜索引擎、 LIDA：轻量交互式对话标注工具 、aili - the fastest in-memory index in the East 东半球最快并发索引 、知识图谱车音工作项目、自然语言生成资源大全 、中日韩分词库mecab的Python接口库、中文文本摘要/关键词提取、汉字字符特征提取器 (featurizer)，提取汉字的特征（发音特征、字形特征）用做深度学习的特征、中文生成任务基准测评 、中文缩写数据集、中文任务基准测评 - 代表性的数据集-基准(预训练)模型-语料库-baseline-工具包-排行榜、PySS3：面向可解释AI的SS3文本分类器机器可视化工具 、中文NLP数据集列表、COPE - 格律诗编辑程序、doccano：基于网页的开源协同多语言文本标注工具 、PreNLP：自然语言预处理库、简单的简历解析器，用来从简历中提取关键信息、用于中文闲聊的GPT2模型：GPT2-chitchat、基于检索聊天机器人多轮响应选择相关资源列表(Leaderboards、Datasets、Papers)、(Colab)抽象文本摘要实现集锦(教程 、词语拼音数据、高效模糊搜索工具、NLP数据增广资源集、微软对话机器人框架 、 GitHub Typo Corpus：大规模GitHub多语言拼写错误/语法错误数据集、TextCluster：短文本聚类预处理模块 Short text cluster、面向语音识别的中文文本规范化、BLINK：最先进的实体链接库、BertPunc：基于BERT的最先进标点修复模型、Tokenizer：快速、可定制的文本词条化库、中文语言理解测评基准，包括代表性的数据集、基准(预训练)模型、语料库、排行榜、spaCy 医学文本挖掘与信息提取 、 NLP任务示例项目代码集、 python拼写检查库、chatbot-list - 行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍、语音质量评价指标(MOSNet, BSSEval, STOI, PESQ, SRMR)、 用138GB语料训练的法文RoBERTa预训练语言模型 、BERT-NER-Pytorch：三种不同模式的BERT中文NER实验、无道词典 - 有道词典的命令行版本，支持英汉互查和在线查询、2019年NLP亮点回顾、 Chinese medical dialogue data 中文医疗对话数据集 、最好的汉字数字(中文数字)-阿拉伯数字转换工具、 基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧、awesome-nlp-sentiment-analysis - 情感分析、情绪原因识别、评价对象和评价词抽取、LineFlow：面向所有深度学习框架的NLP数据高效加载器、中文医学NLP公开资源整理 、MedQuAD：(英文)医学问答数据集、将自然语言数字串解析转换为整数和浮点数、Transfer Learning in Natural Language Processing (NLP) 、面向语音识别的中文/英文发音辞典、Tokenizers：注重性能与多功能性的最先进分词器、CLUENER 细粒度命名实体识别 Fine Grained Named Entity Recognition、 基于BERT的中文命名实体识别、中文谣言数据库、NLP数据集/基准任务大列表、nlp相关的一些论文及代码, 包括主题模型、词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算等，涉及到各种与nlp相关的算法，基于keras和tensorflow 、Python文本挖掘/NLP实战示例、 Blackstone：面向非结构化法律文本的spaCy pipeline和NLP模型通过同义词替换实现文本“变脸” 、中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model 、albert-chinese-ner - 用预训练语言模型ALBERT做中文NER 、基于GPT2的特定主题文本生成/文本增广、开源预训练语言模型合集、多语言句向量包、编码、标记和实现：一种可控高效的文本生成方法、 英文脏话大列表 、attnvis：GPT2、BERT等transformer语言模型注意力交互可视化、CoVoST：Facebook发布的多语种语音-文本翻译语料库，包括11种语言(法语、德语、荷兰语、俄语、西班牙语、意大利语、土耳其语、波斯语、瑞典语、蒙古语和中文)的语音、文字转录及英文译文、Jiagu自然语言处理工具 - 以BiLSTM等模型为基础，提供知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类等功能、用unet实现对文档表格的自动检测，表格重建、NLP事件提取文献资源列表 、 金融领域自然语言处理研究资源大列表、CLUEDatasetSearch - 中英文NLP数据集：搜索所有中文NLP数据集，附常用英文NLP数据集 、medical_NER - 中文医学知识图谱命名实体识别 、(哈佛)讲因果推理的免费书、知识图谱相关学习资料/数据集/工具资源大列表、Forte：灵活强大的自然语言处理pipeline工具集 、Python字符串相似性算法库、PyLaia：面向手写文档分析的深度学习工具包、TextFooler：针对文本分类/推理的对抗文本生成模块、Haystack：灵活、强大的可扩展问答(QA)框架、中文关键短语抽取工具",
"url": "https://github.com/fighting41love/funNLP",
"stars": 82957,
"forks": 15278,
"open_issues": 53,
"watchers": 82957,
"language": "Python",
"topics": [
],
"license": null,
"is_fork": false,
"is_archived": false,
"created_at": "2018-08-21T11:20:39Z",
"updated_at": "2026-09-08T15:59:02Z",
"pushed_at": "2024-05-10T07:38:24Z",
"homepage": "https://zhuanlan.zhihu.com/yangyangfuture",
"default_branch": "master",
"size_kb": 174188,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "josephmisiti/awesome-machine-learning",
"name": "awesome-machine-learning",
"owner": "josephmisiti",
"description": "A curated list of awesome Machine Learning frameworks, libraries and software.",
"url": "https://github.com/josephmisiti/awesome-machine-learning",
"stars": 74278,
"forks": 15641,
"open_issues": 34,
"watchers": 74278,
"language": "Python",
"topics": [
],
"license": "NOASSERTION",
"is_fork": false,
"is_archived": false,
"created_at": "2014-07-15T19:11:19Z",
"updated_at": "2026-09-08T16:03:13Z",
"pushed_at": "2026-09-02T19:29:21Z",
"homepage": null,
"default_branch": "master",
"size_kb": 2685,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "scikit-learn/scikit-learn",
"name": "scikit-learn",
"owner": "scikit-learn",
"description": "scikit-learn: machine learning in Python",
"url": "https://github.com/scikit-learn/scikit-learn",
"stars": 67217,
"forks": 27375,
"open_issues": 2149,
"watchers": 67217,
"language": "Python",
"topics": [
"data-analysis",
"data-science",
"machine-learning",
"python",
"statistics"
],
"license": "BSD-3-Clause",
"is_fork": false,
"is_archived": false,
"created_at": "2010-08-17T09:43:38Z",
"updated_at": "2026-09-08T15:44:19Z",
"pushed_at": "2026-09-08T10:24:38Z",
"homepage": "https://scikit-learn.org",
"default_branch": "main",
"size_kb": 187029,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "gradio-app/gradio",
"name": "gradio",
"owner": "gradio-app",
"description": "Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!",
"url": "https://github.com/gradio-app/gradio",
"stars": 43492,
"forks": 3591,
"open_issues": 161,
"watchers": 43492,
"language": "Python",
"topics": [
"data-analysis",
"data-science",
"data-visualization",
"deep-learning",
"deploy",
"gradio",
"gradio-interface",
"interface",
"machine-learning",
"models",
"python",
"python-notebook",
"ui",
"ui-components"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2018-12-19T08:24:04Z",
"updated_at": "2026-09-08T16:05:59Z",
"pushed_at": "2026-09-08T16:35:58Z",
"homepage": "http://www.gradio.app",
"default_branch": "main",
"size_kb": 359182,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code",
"name": "500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code",
"owner": "ashishpatel26",
"description": "500 AI Machine learning Deep learning Computer vision NLP Projects with code",
"url": "https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code",
"stars": 36768,
"forks": 7478,
"open_issues": 68,
"watchers": 36768,
"language": null,
"topics": [
"artificial-intelligence",
"artificial-intelligence-projects",
"awesome",
"computer-vision",
"computer-vision-project",
"data-science",
"deep-learning",
"deep-learning-project",
"machine-learning",
"machine-learning-projects",
"nlp",
"nlp-projects",
"python"
],
"license": null,
"is_fork": false,
"is_archived": false,
"created_at": "2021-01-02T12:08:37Z",
"updated_at": "2026-09-08T16:49:29Z",
"pushed_at": "2025-08-01T11:54:09Z",
"homepage": null,
"default_branch": "main",
"size_kb": 879,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "TheAlgorithms/C-Plus-Plus",
"name": "C-Plus-Plus",
"owner": "TheAlgorithms",
"description": "Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.",
"url": "https://github.com/TheAlgorithms/C-Plus-Plus",
"stars": 34643,
"forks": 7861,
"open_issues": 24,
"watchers": 34643,
"language": "C++",
"topics": [
"algorithm",
"algorithm-competitions",
"algorithms-implemented",
"artificial-intelligence-algorithms",
"computer-science",
"cpp",
"data-structures",
"educational",
"instructor-materials",
"interview-preparation",
"interview-questions",
"machine-learning",
"machine-learning-algorithms",
"mathematics",
"search",
"sort"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2016-07-16T10:19:45Z",
"updated_at": "2026-09-08T16:32:37Z",
"pushed_at": "2025-10-11T14:51:22Z",
"homepage": "https://thealgorithms.github.io/C-Plus-Plus",
"default_branch": "master",
"size_kb": 140614,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "lutzroeder/netron",
"name": "netron",
"owner": "lutzroeder",
"description": "Visualizer for neural network, deep learning and machine learning models",
"url": "https://github.com/lutzroeder/netron",
"stars": 33450,
"forks": 3185,
"open_issues": 18,
"watchers": 33450,
"language": "JavaScript",
"topics": [
"ai",
"coreml",
"deep-learning",
"deeplearning",
"keras",
"machine-learning",
"machinelearning",
"ml",
"neural-network",
"numpy",
"onnx",
"pytorch",
"safetensors",
"tensorflow",
"tensorflow-lite",
"visualizer"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2010-12-26T12:53:43Z",
"updated_at": "2026-09-08T16:00:45Z",
"pushed_at": "2026-09-07T22:43:35Z",
"homepage": "https://netron.app",
"default_branch": "main",
"size_kb": 98628,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "eriklindernoren/ML-From-Scratch",
"name": "ML-From-Scratch",
"owner": "eriklindernoren",
"description": "Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.",
"url": "https://github.com/eriklindernoren/ML-From-Scratch",
"stars": 32750,
"forks": 5484,
"open_issues": 78,
"watchers": 32750,
"language": "Python",
"topics": [
"data-mining",
"data-science",
"deep-learning",
"deep-reinforcement-learning",
"genetic-algorithm",
"machine-learning",
"machine-learning-from-scratch"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2017-02-05T12:11:23Z",
"updated_at": "2026-09-08T16:57:02Z",
"pushed_at": "2023-10-15T06:05:06Z",
"homepage": null,
"default_branch": "master",
"size_kb": 553,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "eugeneyan/applied-ml",
"name": "applied-ml",
"owner": "eugeneyan",
"description": "📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.",
"url": "https://github.com/eugeneyan/applied-ml",
"stars": 30115,
"forks": 3990,
"open_issues": 8,
"watchers": 30115,
"language": null,
"topics": [
"applied-data-science",
"applied-machine-learning",
"computer-vision",
"data-discovery",
"data-engineering",
"data-quality",
"data-science",
"deep-learning",
"machine-learning",
"natural-language-processing",
"production",
"recsys",
"reinforcement-learning",
"search"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2020-07-04T18:57:47Z",
"updated_at": "2026-09-08T15:26:04Z",
"pushed_at": "2024-07-18T22:41:02Z",
"homepage": null,
"default_branch": "main",
"size_kb": 397,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "ZuzooVn/machine-learning-for-software-engineers",
"name": "machine-learning-for-software-engineers",
"owner": "ZuzooVn",
"description": "A complete daily plan for studying to become a machine learning engineer.",
"url": "https://github.com/ZuzooVn/machine-learning-for-software-engineers",
"stars": 28870,
"forks": 6144,
"open_issues": 32,
"watchers": 28870,
"language": null,
"topics": [
"artificial-intelligence",
"deep-learning",
"machine-learning",
"machine-learning-algorithms",
"software-engineer"
],
"license": "CC-BY-SA-4.0",
"is_fork": false,
"is_archived": false,
"created_at": "2016-10-09T21:20:25Z",
"updated_at": "2026-09-08T08:57:30Z",
"pushed_at": "2024-06-11T04:49:29Z",
"homepage": "https://www.codementor.io/zuzoovn/how-i-plan-to-become-a-machine-learning-engineer-a4metbcuk",
"default_branch": "master",
"size_kb": 471,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "Ebazhanov/linkedin-skill-assessments-quizzes",
"name": "linkedin-skill-assessments-quizzes",
"owner": "Ebazhanov",
"description": "Full reference of LinkedIn answers 2024 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers ",
"url": "https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes",
"stars": 28827,
"forks": 12888,
"open_issues": 20,
"watchers": 28827,
"language": "Python",
"topics": [
"answers",
"assessment",
"english",
"exam",
"france",
"german",
"golang",
"hacktoberfest",
"hacktoberfest2020",
"hacktoberfest2021",
"hacktoberfest2022",
"hacktoberfest2023",
"hacktoberfest2024",
"hacktoberfest2025",
"linkedin",
"linkedin-questions",
"quiz",
"quiz-questions",
"skills"
],
"license": "AGPL-3.0",
"is_fork": false,
"is_archived": false,
"created_at": "2019-07-13T19:52:56Z",
"updated_at": "2026-09-08T13:10:56Z",
"pushed_at": "2026-08-29T17:24:40Z",
"homepage": "https://ebazhanov.github.io/linkedin-skill-assessments-quizzes/",
"default_branch": "main",
"size_kb": 155538,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "harvard-edge/cs249r_book",
"name": "cs249r_book",
"owner": "harvard-edge",
"description": "Machine Learning Systems",
"url": "https://github.com/harvard-edge/cs249r_book",
"stars": 28163,
"forks": 3571,
"open_issues": 16,
"watchers": 28163,
"language": "Python",
"topics": [
"artificial-intelligence",
"cloud-ml",
"computer-systems",
"courseware",
"deep-learning",
"edge-machine-learning",
"embedded-ml",
"machine-learning",
"machine-learning-systems",
"mobile-ml",
"textbook",
"tinyml"
],
"license": "NOASSERTION",
"is_fork": false,
"is_archived": false,
"created_at": "2023-09-06T19:31:06Z",
"updated_at": "2026-09-08T14:29:51Z",
"pushed_at": "2026-09-08T12:38:10Z",
"homepage": "http://mlsysbook.ai/",
"default_branch": "dev",
"size_kb": 3170571,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "shap/shap",
"name": "shap",
"owner": "shap",
"description": "A game theoretic approach to explain the output of any machine learning model.",
"url": "https://github.com/shap/shap",
"stars": 25729,
"forks": 3740,
"open_issues": 983,
"watchers": 25729,
"language": "Jupyter Notebook",
"topics": [
"deep-learning",
"explainability",
"gradient-boosting",
"interpretability",
"machine-learning",
"shap",
"shapley"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2016-11-22T19:17:08Z",
"updated_at": "2026-09-08T13:36:37Z",
"pushed_at": "2026-09-07T22:47:27Z",
"homepage": "https://shap.readthedocs.io",
"default_branch": "main",
"size_kb": 299116,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "trekhleb/homemade-machine-learning",
"name": "homemade-machine-learning",
"owner": "trekhleb",
"description": "🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained",
"url": "https://github.com/trekhleb/homemade-machine-learning",
"stars": 24783,
"forks": 4189,
"open_issues": 30,
"watchers": 24783,
"language": "Jupyter Notebook",
"topics": [
"algorithm",
"jupyter",
"jupyter-notebook",
"machine-learning",
"machine-learning-algorithms",
"machinelearning",
"python"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2018-11-01T04:34:19Z",
"updated_at": "2026-09-08T03:30:48Z",
"pushed_at": "2025-11-23T04:22:46Z",
"homepage": null,
"default_branch": "master",
"size_kb": 14467,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "PaddlePaddle/Paddle",
"name": "Paddle",
"owner": "PaddlePaddle",
"description": "PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）",
"url": "https://github.com/PaddlePaddle/Paddle",
"stars": 24074,
"forks": 6020,
"open_issues": 1497,
"watchers": 24074,
"language": "C++",
"topics": [
"deep-learning",
"distributed-training",
"efficiency",
"machine-learning",
"neural-network",
"paddlepaddle",
"python",
"scalability"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2016-08-15T06:59:08Z",
"updated_at": "2026-09-08T10:32:23Z",
"pushed_at": "2026-09-08T09:51:45Z",
"homepage": "http://www.paddlepaddle.org/",
"default_branch": "develop",
"size_kb": 557821,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "lukasmasuch/best-of-ml-python",
"name": "best-of-ml-python",
"owner": "lukasmasuch",
"description": "🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.",
"url": "https://github.com/lukasmasuch/best-of-ml-python",
"stars": 23794,
"forks": 3150,
"open_issues": 57,
"watchers": 23794,
"language": null,
"topics": [
"automl",
"chatgpt",
"data-analysis",
"data-science",
"data-visualization",
"data-visualizations",
"deep-learning",
"gpt",
"gpt-3",
"jax",
"keras",
"machine-learning",
"ml",
"nlp",
"python",
"pytorch",
"scikit-learn",
"tensorflow",
"transformer"
],
"license": "CC-BY-SA-4.0",
"is_fork": false,
"is_archived": false,
"created_at": "2020-11-29T19:41:36Z",
"updated_at": "2026-09-08T15:16:42Z",
"pushed_at": "2026-09-03T14:15:42Z",
"homepage": "https://ml-python.best-of.org",
"default_branch": "main",
"size_kb": 21391,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "TheAlgorithms/C",
"name": "C",
"owner": "TheAlgorithms",
"description": "Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.",
"url": "https://github.com/TheAlgorithms/C",
"stars": 22398,
"forks": 4769,
"open_issues": 20,
"watchers": 22398,
"language": "C",
"topics": [
"algorithm-challenges",
"algorithm-competitions",
"algorithms",
"c",
"community-driven",
"computer-science",
"data-structures",
"datastructures",
"education",
"educational",
"hacktoberfest",
"interview",
"interview-questions",
"learn-to-code",
"machine-learning",
"machine-learning-algorithms",
"mathematics",
"search",
"sort"
],
"license": "GPL-3.0",
"is_fork": false,
"is_archived": false,
"created_at": "2016-07-16T10:17:03Z",
"updated_at": "2026-09-08T16:32:44Z",
"pushed_at": "2024-08-07T21:56:16Z",
"homepage": "https://thealgorithms.github.io/C",
"default_branch": "master",
"size_kb": 20365,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "k4yt3x/video2x",
"name": "video2x",
"owner": "k4yt3x",
"description": "A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018.",
"url": "https://github.com/k4yt3x/video2x",
"stars": 21534,
"forks": 1864,
"open_issues": 132,
"watchers": 21534,
"language": "C++",
"topics": [
"anime4k",
"frame-interpolation",
"machine-learning",
"neural-networks",
"realcugan",
"realesrgan",
"rife",
"super-resoluion",
"upscale-video",
"vulkan"
],
"license": "AGPL-3.0",
"is_fork": false,
"is_archived": false,
"created_at": "2018-02-24T16:34:41Z",
"updated_at": "2026-09-08T16:22:00Z",
"pushed_at": "2026-03-07T15:39:42Z",
"homepage": "https://docs.video2x.org",
"default_branch": "master",
"size_kb": 574222,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "onnx/onnx",
"name": "onnx",
"owner": "onnx",
"description": "Open standard for machine learning interoperability",
"url": "https://github.com/onnx/onnx",
"stars": 21429,
"forks": 4023,
"open_issues": 237,
"watchers": 21429,
"language": "Python",
"topics": [
"ai",
"artificial-intelligence",
"deep-learning",
"deep-neural-networks",
"dnn",
"keras",
"machine-learning",
"ml",
"neural-network",
"onnx",
"pytorch",
"scikit-learn",
"tensorflow"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2017-09-07T04:53:45Z",
"updated_at": "2026-09-08T16:36:58Z",
"pushed_at": "2026-09-08T16:19:03Z",
"homepage": "https://onnx.ai/",
"default_branch": "main",
"size_kb": 73489,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "RasaHQ/rasa",
"name": "rasa",
"owner": "RasaHQ",
"description": "💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants",
"url": "https://github.com/RasaHQ/rasa",
"stars": 21318,
"forks": 4913,
"open_issues": 153,
"watchers": 21318,
"language": "Python",
"topics": [
"bot",
"bot-framework",
"botkit",
"bots",
"chatbot",
"chatbots",
"chatbots-framework",
"conversation-driven-development",
"conversational-agents",
"conversational-ai",
"conversational-bots",
"machine-learning",
"machine-learning-library",
"mitie",
"natural-language-processing",
"nlp",
"nlu",
"rasa",
"spacy",
"wit"
],
"license": "Apache-2.0",
"is_fork": false,
"is_archived": false,
"created_at": "2016-10-14T12:27:49Z",
"updated_at": "2026-09-08T02:53:20Z",
"pushed_at": "2026-07-24T07:39:04Z",
"homepage": "https://rasa.com/docs/rasa/",
"default_branch": "3.6.x",
"size_kb": 1707110,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "EthicalML/awesome-production-machine-learning",
"name": "awesome-production-machine-learning",
"owner": "EthicalML",
"description": "A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning",
"url": "https://github.com/EthicalML/awesome-production-machine-learning",
"stars": 20897,
"forks": 2597,
"open_issues": 31,
"watchers": 20897,
"language": null,
"topics": [
"awesome",
"awesome-list",
"data-mining",
"deep-learning",
"explainability",
"interpretability",
"large-scale-machine-learning",
"large-scale-ml",
"machine-learning",
"machine-learning-operations",
"ml-operations",
"ml-ops",
"mlops",
"privacy-preserving",
"privacy-preserving-machine-learning",
"privacy-preserving-ml",
"production-machine-learning",
"production-ml",
"responsible-ai"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2018-08-15T14:28:41Z",
"updated_at": "2026-09-08T09:04:06Z",
"pushed_at": "2026-09-06T16:54:10Z",
"homepage": "https://ethicalml.github.io/awesome-production-machine-learning",
"default_branch": "master",
"size_kb": 3009,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "stefan-jansen/machine-learning-for-trading",
"name": "machine-learning-for-trading",
"owner": "stefan-jansen",
"description": "Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.",
"url": "https://github.com/stefan-jansen/machine-learning-for-trading",
"stars": 20842,
"forks": 5594,
"open_issues": 5,
"watchers": 20842,
"language": "Jupyter Notebook",
"topics": [
"algorithmic-trading",
"artificial-intelligence",
"backtesting",
"data-science",
"deep-learning",
"finance",
"investment",
"investment-strategies",
"large-language-models",
"machine-learning",
"ml4t-workflow",
"polars",
"quantitative-finance",
"reinforcement-learning",
"synthetic-data",
"trading",
"trading-agent",
"trading-strategies"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2018-05-09T12:33:08Z",
"updated_at": "2026-09-08T14:27:30Z",
"pushed_at": "2026-09-08T17:02:20Z",
"homepage": "https://ml4trading.io",
"default_branch": "main",
"size_kb": 1136029,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "afshinea/stanford-cs-229-machine-learning",
"name": "stanford-cs-229-machine-learning",
"owner": "afshinea",
"description": "VIP cheatsheets for Stanford's CS 229 Machine Learning",
"url": "https://github.com/afshinea/stanford-cs-229-machine-learning",
"stars": 20196,
"forks": 4256,
"open_issues": 20,
"watchers": 20196,
"language": null,
"topics": [
"cheatsheet",
"cs229",
"data-science",
"deep-learning",
"machine-learning",
"ml-cheatsheet",
"supervised-learning",
"unsupervised-learning"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2018-08-04T23:01:00Z",
"updated_at": "2026-09-08T16:00:16Z",
"pushed_at": "2020-05-20T04:57:25Z",
"homepage": "https://stanford.edu/~shervine/teaching/cs-229",
"default_branch": "master",
"size_kb": 38339,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "Unity-Technologies/ml-agents",
"name": "ml-agents",
"owner": "Unity-Technologies",
"description": "The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.",
"url": "https://github.com/Unity-Technologies/ml-agents",
"stars": 19664,
"forks": 4487,
"open_issues": 21,
"watchers": 19664,
"language": "C#",
"topics": [
"deep-learning",
"deep-reinforcement-learning",
"machine-learning",
"neural-networks",
"reinforcement-learning",
"unity",
"unity3d"
],
"license": "NOASSERTION",
"is_fork": false,
"is_archived": false,
"created_at": "2017-09-08T21:09:04Z",
"updated_at": "2026-09-08T10:05:21Z",
"pushed_at": "2026-09-02T18:38:03Z",
"homepage": "https://unity.com/products/machine-learning-agents",
"default_branch": "develop",
"size_kb": 3028250,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "stas00/ml-engineering",
"name": "ml-engineering",
"owner": "stas00",
"description": "Machine Learning Engineering Open Book",
"url": "https://github.com/stas00/ml-engineering",
"stars": 18935,
"forks": 1243,
"open_issues": 4,
"watchers": 18935,
"language": "Python",
"topics": [
"ai",
"debugging",
"gpus",
"inference",
"large-language-models",
"llm",
"machine-learning",
"machine-learning-engineering",
"mlops",
"network",
"pytorch",
"scalability",
"slurm",
"storage",
"training",
"transformers"
],
"license": "CC-BY-SA-4.0",
"is_fork": false,
"is_archived": false,
"created_at": "2020-09-02T19:23:01Z",
"updated_at": "2026-09-08T14:41:10Z",
"pushed_at": "2026-09-04T00:12:37Z",
"homepage": "https://stasosphere.com/machine-learning/",
"default_branch": "master",
"size_kb": 25183,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "lightgbm-org/LightGBM",
"name": "LightGBM",
"owner": "lightgbm-org",
"description": "A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.",
"url": "https://github.com/lightgbm-org/LightGBM",
"stars": 18753,
"forks": 4065,
"open_issues": 511,
"watchers": 18753,
"language": "C++",
"topics": [
"data-mining",
"decision-trees",
"distributed",
"gbdt",
"gbm",
"gbrt",
"gradient-boosting",
"kaggle",
"lightgbm",
"machine-learning",
"parallel",
"python",
"r"
],
"license": "MIT",
"is_fork": false,
"is_archived": false,
"created_at": "2016-08-05T05:45:50Z",
"updated_at": "2026-09-08T16:09:01Z",
"pushed_at": "2026-09-08T16:05:39Z",
"homepage": "https://lightgbm.readthedocs.io/en/latest/",
"default_branch": "main",
"size_kb": 25595,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
},
{
"full_name": "ujjwalkarn/Machine-Learning-Tutorials",
"name": "Machine-Learning-Tutorials",
"owner": "ujjwalkarn",
"description": "machine learning and deep learning tutorials, articles and other resources ",
"url": "https://github.com/ujjwalkarn/Machine-Learning-Tutorials",
"stars": 18169,
"forks": 3966,
"open_issues": 47,
"watchers": 18169,
"language": null,
"topics": [
"awesome",
"awesome-list",
"deep-learning",
"deep-learning-tutorial",
"deep-neural-networks",
"deeplearning",
"list",
"machine-learning",
"machinelearning",
"neural-network",
"neural-networks"
],
"license": "CC0-1.0",
"is_fork": false,
"is_archived": false,
"created_at": "2015-09-12T14:51:38Z",
"updated_at": "2026-09-07T14:00:35Z",
"pushed_at": "2024-06-12T08:11:59Z",
"homepage": "http://ujjwalkarn.github.io/Machine-Learning-Tutorials",
"default_branch": "master",
"size_kb": 9024,
"scraped_at": "2026-09-08T17:12:59.689254+00:00"
}
]
}
GET
/v1/github/search/repositories1 credit / run VerifiedSearch Repositories
Search public repositories by keyword.
Handled for you:Rate Limit
Parameters
| Name | Type | Req | Description |
|---|---|---|---|
| q | string | yes | Search query (GitHub search syntax). |
| Advanced filters | |||
| sort | string | no | stars | forks | help-wanted-issues | updated (default: stars) |
| limit | integer | no | Results per page, 1-100. (default: 30) |