├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 howie.hu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | - [mlhub123](#mlhub123) 6 | - [导航](#导航) 7 | - [新闻资讯](#新闻资讯) 8 | - [工具服务](#工具服务) 9 | - [社区交流](#社区交流) 10 | - [优质博文](#优质博文) 11 | - [资源检索](#资源检索) 12 | - [比赛实践](#比赛实践) 13 | - [资源](#资源) 14 | - [课程学习](#课程学习) 15 | - [资源收集](#资源收集) 16 | - [开源书籍](#开源书籍) 17 | - [实战项目](#实战项目) 18 | - [方法论](#方法论) 19 | - [文档](#文档) 20 | - [Python](#python) 21 | - [C \& C++](#c--c) 22 | 23 | 24 | 25 | # mlhub123 26 | 27 | ![aHb1LO](https://raw.githubusercontent.com/howie6879/oss/master/images/aHb1LO.png) 28 | 29 | 机器学习网站导航以及资源，欢迎**PR提供资源**： 30 | - 网站：[https://www.mlhub123.com/](https://www.mlhub123.com/) 31 | - 进微信群交流：备注mlhub进群 - [mlhub](https://ws1.sinaimg.cn/large/007i3XCUgy1fwgr8qhjz7j306506faag.jpg) 32 | - Telegram群组：[欢迎加入](https://t.me/joinchat/F6XKShFSdCUHuo5Rvoj4Jg)，资源多多~ 33 | 34 | ## 导航 35 | 36 | ### 新闻资讯 37 | 38 | - [Analytics Vidhya](https://www.analyticsvidhya.com/blog/?from=www.mlhub123.com): 为数据科学专业人员提供基于社区的知识门户 39 | - [Distill](https://distill.pub/?from=www.mlhub123.com): 展示机器学习的最新文章 40 | - [Google News](https://news.google.com/topics/CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE?hl=en-US&gl=US&ceid=US%3Aen?from=www.mlhub123.com): Google News Machine learning 41 | - [kdnuggets](https://www.kdnuggets.com/?from=www.mlhub123.com): Machine Learning, Data Science, Big Data, Analytics, AI 42 | - [MIT News](http://news.mit.edu/topic/machine-learning?from=www.mlhub123.com): Machine learning | MIT News 43 | - [机器之心](https://www.jiqizhixin.com?from=www.mlhub123.com): 机器之心 | 全球人工智能信息服务 44 | - [雷锋网](https://www.leiphone.com/?from=www.mlhub123.com): 雷锋网 | 读懂智能，未来 45 | - [数据分析网](https://www.afenxi.com?from=www.mlhub123.com): 数据分析网 - 大数据学习交流第一平台 46 | - [知乎主题](https://www.zhihu.com/topic/19559450/hot?from=www.mlhub123.com): 知乎机器学习热门主题 47 | - [专知](http://www.zhuanzhi.ai?from=www.mlhub123.com): AI知识分发服务平台 48 | - [aminer](https://www.aminer.cn/research_report/articlelist?from=www.mlhub123.com): 科技资讯 49 | 50 | ### 工具服务 51 | 52 | - [chatgpt](https://ai.com/?from=www.mlhub123.com): OpenAI开发的人工智能聊天机器人程序 53 | - [codeocean](https://codeocean.com/?from=www.mlhub123.com): 可重现性代码共享平台 54 | - [colab](https://colab.research.google.com/?from=www.mlhub123.com): 免费使用GPU的在线工作平台 55 | - [ECharts](https://echarts.apache.org/?from=www.mlhub123.com): 使用JavaScript实现的开源可视化库 56 | - [excalidraw](https://excalidraw.com/?from=www.mlhub123.com): 绘图软件 57 | - [drawio](https://draw.io?from=www.mlhub123.com) 开源免费的绘图工具 58 | - [Khroma](http://khroma.co/?from=www.mlhub123.com): 人工智能配色网站 59 | 60 | ### 社区交流 61 | 62 | - [AIQ](http://www.6aiq.com/?from=www.mlhub123.com): 机器学习大数据技术社区 63 | - [DataTau](https://www.datatau.com?from=www.mlhub123.com): 人工智能领域的Hacker News 64 | - [MathOverflow](https://mathoverflow.net?from=www.mlhub123.com): 数学知识问答社区 65 | - [Medium](https://medium.com/?from=www.mlhub123.com): 一个涵盖人工智能、机器学习和深度学习相关领域的自由、开放平台 66 | - [PaperWeekly](http://www.paperweekly.site?from=www.mlhub123.com): 一个推荐、解读、讨论和报道人工智能前沿论文成果的学术平台 67 | - [Quora](https://www.quora.com/pinned/Machine-Learning?from=www.mlhub123.com): Quora | 机器学习主题 68 | - [Reddit](https://www.reddit.com/r/MachineLearning/?from=www.mlhub123.com): Reddit | 机器学习板块 69 | - [ShortScience](http://www.shortscience.org?from=www.mlhub123.com): 用最简单的篇幅去概况科学著作 70 | - [Twitter](https://twitter.com/StatMLPapers?from=www.mlhub123.com): Twitter | 机器学习论文版块 71 | 72 | ### 优质博文 73 | 74 | - [Google AI Blog](https://ai.googleblog.com/?from=www.mlhub123.com): 谷歌AI博客 75 | - [handong1587](https://handong1587.github.io/?from=www.mlhub123.com): 深度学习各个方向资源汇总，及各大顶级会议/期刊资源 76 | - [Machine Learning Mastery](https://machinelearningmastery.com/blog?from=www.mlhub123.com): 帮助开发人员使用机器学习的知识解决复杂的问题 77 | - [paralleldots](https://blog.paralleldots.com/?from=www.mlhub123.com)：一个提供随时可用的一流AI解决方案的博客 78 | - [tornadomeet的博客](https://www.cnblogs.com/tornadomeet/archive/2012/06/24/2560261.html?from=www.mlhub123.com): 很详细的ML&DL学习博客 79 | - [wildml](http://www.wildml.com/?from=www.mlhub123.com)：Artificial Intelligence, Deep Learning, and NLP 80 | - [爱可可-爱生活](https://weibo.com/fly51fly?topnav=1&wvr=6&topsug=1?from=www.mlhub123.com): 知名互联网资讯博主 81 | - [超智能体](https://zhuanlan.zhihu.com/YJango?from=www.mlhub123.com): 分享最通俗易懂的深度学习教程 82 | - [人工智能笔记](https://zhuanlan.zhihu.com/ainote?from=www.mlhub123.com): 人工智能从入门到AI统治世界 83 | 84 | ### 资源检索 85 | 86 | - [arXiv](https://arxiv.org?from=www.mlhub123.com): 康奈尔大学运营的学术预印本发布的平台 87 | - [Arxiv Sanity](http://www.arxiv-sanity.com/?from=www.mlhub123.com): 论文查询推荐 88 | - [bifrost](https://datasets.bifrost.ai/?from=www.mlhub123.com): 提供人物、自动驾驶汽车、零售、无人机等六大类别数据集检索 89 | - [connected papers](https://www.connectedpapers.com/?from=www.mlhub123.com): 用可视化的形式发现&浏览论文 90 | - [Hugging Face](https://huggingface.co/?from=www.mlhub123.com): 机器学习界的github，提供预训练模型和数据集等资源 91 | - [iData](https://www.cn-ki.net/?from=www.mlhub123.com): iData-知识检索 92 | - [lexica](https://lexica.art/?from=www.mlhub123.com): 超过10M + Stable Diffusion 图像和 Prompts 93 | - [NLP Index](https://index.quantumstat.com/?from=www.mlhub123.com): 实用的NLP索引工具 94 | - [Papers with Code](https://paperswithcode.com/?from=www.mlhub123.com): 将论文与开源代码实现结合 95 | - [phind](https://phind.com/?from=www.mlhub123.com): The AI search engine for developers 96 | - [SCI-HUB](https://sci-hub.ru/?from=www.mlhub123.com): 找论文必备 97 | - [Semantic Scholar](https://www.semanticscholar.org/?from=www.mlhub123.com): 致力于解决信息超载的学术文献搜索引擎 98 | 99 | ### 比赛实践 100 | 101 | - [DataCastle](http://www.pkbigdata.com/?from=www.mlhub123.com): 中国领先的数据科学竞赛平台 102 | - [DataFountain](http://www.datafountain.cn/#/?from=www.mlhub123.com): DF,CCF指定专业大数据竞赛平台 103 | - [Kaggle](https://www.kaggle.com/?from=www.mlhub123.com): 为数据科学家提供举办机器学习竞赛 104 | - [KDD-CUP](http://www.kdd.org/kdd-cup?from=www.mlhub123.com): 国际知识发现和数据挖掘竞赛 105 | - [赛氪网](http://www.saikr.com/?from=www.mlhub123.com): 汇集以高校竞赛为主，活动、社区为辅的大学生竞赛活动平台 106 | - [天池大数据](https://tianchi.aliyun.com/?from=www.mlhub123.com): 大数据竞赛、大数据解决方案、数据科学家社区、人工智能、机器学习 107 | 108 | 109 | ## 资源 110 | 111 | ### 课程学习 112 | 113 | - [data-science-complete-tutorial](https://github.com/zekelabs/data-science-complete-tutorial?from=www.mlhub123.com): 数据科学完整入门指南 114 | - [David Silver](https://v.youku.com/v_show/id_XMjcwMDQyOTcxMg==.html?spm=a2h0j.11185381.listitem_page1.5!4~A&&f=49376145?from=www.mlhub123.com): David Silver 深度强化学习课程 115 | - [fast.ai](http://www.fast.ai/?from=www.mlhub123.com): Making neural nets uncool again 116 | - [hanbt](https://www.zybuluo.com/hanbingtao/note/433855?from=www.mlhub123.com): 零基础入门深度学习，深入浅出，很不错的入门教程 117 | - [Juicy Big Data](https://github.com/datawhalechina/juicy-bigdata?from=www.mlhub123.com): Datawhale大数据处理导论教程 118 | - [liuyubobobo](https://coding.imooc.com/class/169.html?from=www.mlhub123.com): Python3 入门机器学习 119 | - [Metacademy](https://metacademy.org/?from=www.mlhub123.com): 知识点检索并画出通向这个知识点的知识图谱 120 | - [MLEveryday](https://github.com/MLEveryday?from=www.mlhub123.com): machine learning everyday 121 | - [Siraj Raval：时序预测](https://www.kaggle.com/learn/time-series-with-siraj?from=www.mlhub123.com): Kaggle免费课程：时序预测 122 | - [Two Minute Papers](https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg?from=www.mlhub123.com): YouTube | 最简短的语言概况最新的热点论文 123 | - [YSDA nlp_course](https://github.com/yandexdataschool/nlp_course?from=www.mlhub123.com): YSDA course in Natural Language Processing 124 | - [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw?from=www.mlhub123.com): YouTube | 数学基础频道 125 | - [3Blue1Brown 中文](http://space.bilibili.com/88461692/#/?from=www.mlhub123.com): Bilibili | 数学基础频道 126 | - [谷歌：机器学习速成课程](https://developers.google.cn/machine-learning/crash-course/?from=www.mlhub123.com): Google制作的节奏紧凑、内容实用的机器学习简介课程 127 | - [李宏毅](https://speech.ee.ntu.edu.tw/~hylee/index.php?from=www.mlhub123.com): 李宏毅深度学习课程 128 | - [林轩田](https://www.bilibili.com/video/av12469267?from=www.mlhub123.com): 机器学习技法 129 | - [邱锡鹏（复旦大学）](https://github.com/nndl/nndl.github.io?from=www.mlhub123.com): 神经网络与深度学习 130 | - [人工智能公开课合集](https://study.163.com/series/1001461001.htm?from=www.mlhub123.com)人工智能国内外顶尖公开课系列 131 | - [吴恩达](https://study.163.com/course/introduction/1210076550.htm?from=www.mlhub123.com): 机器学习课程 132 | - [吴恩达](https://mooc.study.163.com/smartSpec/detail/1001319001.htm?from=www.mlhub123.com): 深度学习课程 133 | - [徐亦达](https://github.com/roboticcam/machine-learning-notes?from=www.mlhub123.com): 徐亦达老师机器学习课程 134 | - [张子豪（同济）](https://github.com/TommyZihao/zihao_course/?from=www.mlhub123.com): 同济子豪兄的公开课：机器学习+计算机视觉+论文精读 135 | 136 | ### 资源收集 137 | 138 | - [awesome-machine-learning-cn](https://github.com/jobbole/awesome-machine-learning-cn?from=www.mlhub123.com): 机器学习资源大全中文版，包括机器学习领域的框架、库以及软件 139 | - [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets?from=www.mlhub123.com): 各领域公开数据集下载 140 | - [Coursera-ML-AndrewNg-Notes](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes?from=www.mlhub123.com): 吴恩达老师的机器学习课程个人笔记 141 | - [daily-paper-computer-vision](https://github.com/amusi/daily-paper-computer-vision?from=www.mlhub123.com): 记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文 142 | - [DeepLearning-500-questions](https://github.com/scutan90/DeepLearning-500-questions?from=www.mlhub123.com)：深度学习500问 143 | - [deeplearning_ai_books](https://github.com/fengdu78/deeplearning_ai_books?from=www.mlhub123.com): 吴恩达老师的深度学习课程笔记及资源 144 | - [Deep-Learning-Papers-Reading-Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap?from=www.mlhub123.com): 深度学习论文阅读路线图 145 | - [funNLP](https://github.com/fighting41love/funNLP?from=www.mlhub123.com)：中文语料库资源收集项目 146 | - [FunRec](https://datawhalechina.github.io/fun-rec/#/?from=www.mlhub123.com): 推荐算法基础+实战+面经 147 | - [Getting Started in Computer Vision Research](https://sites.google.com/site/mostafasibrahim/research/articles/how-to-start?from=www.mlhub123.com)：计算机视觉研究入门全指南 148 | - [lihang_book_algorithm](https://github.com/WenDesi/lihang_book_algorithm?from=www.mlhub123.com): 《统计学习方法》算法python实现 149 | - [Machine Learning、Deep Learning](https://github.com/ty4z2008/Qix/blob/master/dl.md?from=www.mlhub123.com): ML&DL资料 150 | - [MachineLearning_Python](https://github.com/lawlite19/MachineLearning_Python?from=www.mlhub123.com): 机器学习算法python实现 151 | - [Machine_Learning_Study_Path](https://github.com/linxid/Machine_Learning_Study_Path?from=www.mlhub123.com)：机器学习过程中所看的书，视频和源码 152 | - [ml_cheatsheet](https://github.com/remicnrd/ml_cheatsheet?from=www.mlhub123.com)：机器学习算法速查手册 153 | - [ml_tutorials](https://github.com/MorvanZhou/tutorials?from=www.mlhub123.com): 机器学习相关教程 154 | - [NLP-progress](https://github.com/sebastianruder/NLP-progress?from=www.mlhub123.com)：跟踪NLP各项技术的state-of-the-art进展 155 | - [paper-qa](https://github.com/whitead/paper-qa?from=www.mlhub123.com): 用GPT-3来解读论文的开源项目 156 | - [paper-reading](https://github.com/mli/paper-reading?from=www.mlhub123.com): 深度学习经典、新论文逐段精读 157 | - [papers-we-love](https://github.com/papers-we-love/papers-we-love?from=www.mlhub123.com): 阅读、讨论和学习计算机科学学术论文的社区 158 | - [100-Days-Of-ML-Code 英文版](https://github.com/Avik-Jain/100-Days-Of-ML-Code?from=www.mlhub123.com)：100 Days of Machine Learning Coding as proposed by Siraj Raval 159 | - [100-Days-Of-ML-Code 中文版](https://github.com/MLEveryday/100-Days-Of-ML-Code?from=www.mlhub123.com)：100-Days-Of-ML-Code 中文版 160 | - [系统学习机器学习](https://www.zhihu.com/question/266291909?from=www.mlhub123.com): 系统学习机器学习 161 | - [周志华 - 机器学习](https://github.com/Vay-keen/Machine-learning-learning-notes?from=www.mlhub123.com): 周志华《机器学习》笔记 162 | 163 | ### 开源书籍 164 | - [AiLearning](https://github.com/apachecn/MachineLearning?from=www.mlhub123.com): AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2 165 | - [deeplearningbook-chinese](https://github.com/exacity/deeplearningbook-chinese?from=www.mlhub123.com): 深度学习中文版 166 | - [动手学深度学习](https://github.com/d2l-ai/d2l-zh?from=www.mlhub123.com): 《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。 167 | - [deep_learning_cookbook](https://github.com/DOsinga/deep_learning_cookbook?from=www.mlhub123.com): 深度学习手册 168 | - [hands_on_Ml_with_Sklearn_and_TF](https://github.com/apachecn/hands_on_Ml_with_Sklearn_and_TF?from=www.mlhub123.com): Sklearn与TensorFlow机器学习实用指南 169 | - [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/?from=www.mlhub123.com): 一份指南，教你如何构建具有可解释性的黑盒模型 170 | - [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html?from=www.mlhub123.com): 深度学习开源书籍 171 | - [Neural Networks and Deep Learning](https://github.com/zhanggyb/nndl?from=www.mlhub123.com): 深度学习开源书籍 - 中文 172 | - [PythonDataScienceHandbook](https://github.com/jakevdp/PythonDataScienceHandbook?from=www.mlhub123.com): Python数据科学手册 173 | - [TensorFlow-Course](https://github.com/open-source-for-science/TensorFlow-Course?from=www.mlhub123.com): 简单易学的TensorFlow教程 174 | - [简单粗暴 TensorFlow 2](https://tf.wiki?from=www.mlhub123.com): 一本简明的 TensorFlow 2 入门指导手册 175 | 176 | ### 实战项目 177 | 178 | - [face_recognition](https://github.com/ageitgey/face_recognition?from=www.mlhub123.com): 世界上最简单的人脸识别库 179 | - [style2paints](https://github.com/lllyasviel/style2paints?from=www.mlhub123.com): 线稿自动上色 180 | 181 | ### 方法论 182 | 183 | - [face_recognition](https://space.bilibili.com/344849038/dynamic?from=www.mlhub123.com): 学习观 184 | - [tuning_playbook](https://github.com/google-research/tuning_playbook?from=www.mlhub123.com): 聚焦超参数调整的深度学习调优手册 185 | 186 | ## 文档 187 | 188 | ### Python 189 | 190 | - [Caffe](http://caffe.berkeleyvision.org/?from=www.mlhub123.com): 一个基于表达式，速度和模块化原则创建的深度学习框架 191 | - [Caffe2](https://caffe2.ai/docs/getting-started.html?platform=windows&configuration=compile?from=www.mlhub123.com): Caffe2官方文档 192 | - [Chainer](https://docs.chainer.org/en/stable/?from=www.mlhub123.com): 基于Python的独立的深度学习模型开源框架 193 | - [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/?from=www.mlhub123.com): CNTK官方文档 194 | - [Gensim](https://radimrehurek.com/gensim/index.html?from=www.mlhub123.com): 包含可扩展的统计语义，分析纯文本文档的语义结构，以及检索相似语义的文档等功能 195 | - [Keras](https://keras.io/?from=www.mlhub123.com): Keras官方文档 196 | - [Matplotlib](https://matplotlib.org/stable/tutorials/index.html?from=www.mlhub123.com): Matplotlib官方文档 197 | - [MXNet](https://mxnet.incubator.apache.org/api/python/docs/tutorials/?from=www.mlhub123.com): MXNet官方文档 198 | - [NumPy](http://www.numpy.org/?from=www.mlhub123.com): NumPy官方文档 199 | - [pandas](http://pandas.pydata.org/pandas-docs/stable/?from=www.mlhub123.com): pandas官方文档 200 | - [PyBrain](http://pybrain.org/docs/?from=www.mlhub123.com): 一个模块化的Python机器学习库 201 | - [PyTorch](https://pytorch.org/tutorials/?from=www.mlhub123.com): PyTorch官方文档 202 | - [Seaborn](https://seaborn.pydata.org/?from=www.mlhub123.com): statistical data visualization 203 | - [scikit-learn](http://scikit-learn.org/stable/documentation.html?from=www.mlhub123.com): scikit-learn官方文档 204 | - [Statsmodels](http://www.statsmodels.org/stable/index.html?from=www.mlhub123.com): 用来探索数据，估计统计模型，进行统计测试 205 | - [TensorFlow](https://www.tensorflow.org/tutorials/?from=www.mlhub123.com): TF官方文档 206 | - [Theano](http://deeplearning.net/software/theano/?from=www.mlhub123.com): 允许高效地定义、优化以及评估涉及多维数组的数学表达式 207 | - [openai](https://spinningup.openai.com/en/latest/?from=www.mlhub123.com): 强化学习 208 | 209 | ### C & C++ 210 | - [dlib](http://dlib.net?from=www.mlhub123.com): 实用的机器学习和数据分析工具包 211 | --------------------------------------------------------------------------------