【推荐】一份超全的机器学习&深度学习网站资源清单(105个AI站点)，请收藏！

女爷i 2022-04-16 06:06 250阅读 0赞

### 目录 ###

*  导读
 *  导行
 *   *  新闻咨询
     *  社区交流
     *  优质博文
     *  论文检索
     *  比赛实践
 *  资源
 *   *  课程学习
     *  资源收集
     *  开源书籍
     *  实战项目
 *  文档
 *   *  Python
     *  C & C++
     *  Java & Scala
 *  结束语

# 导读 #

howie6879在Github上维护了一个机器学习网站导航以及资源的目录，包含了新闻资讯、课程、比赛、项目、文档等导航链接，主流的都涵盖到，是深度学习从业者不错的一个导航，欢迎使用！  
网址：[https://github.com/howie6879/mlhub123][https_github.com_howie6879_mlhub123]

# 导行 #

## 新闻咨询 ##

*  Analytics Vidhya ([https://www.analyticsvidhya.com/blog/][https_www.analyticsvidhya.com_blog]): 为数据科学专业人员提供基于社区的知识门户 Distill ([https://distill.pub/][https_distill.pub]): 展示机器学习的最新文章
 *  Google News  
    ([https://news.google.com/topics/CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE?hl=en-US&gl=US&ceid=US%3Aen][https_news.google.com_topics_CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE_hl_en-US_gl_US_ceid_US_3Aen]): Google News Machine learning
 *  MIT News ([http://news.mit.edu/topic/machine-learning][http_news.mit.edu_topic_machine-learning]): Machine learning | MIT News
 *  17bigdata ([http://www.17bigdata.com][http_www.17bigdata.com] ): 专注数据分析、挖掘、大数据相关领域的技术分享、交流
 *  机器之心 ([https://www.jiqizhixin.com][https_www.jiqizhixin.com]): 机器之心 | 全球人工智能信息服务
 *  雷锋网 ([https://www.leiphone.com][https_www.leiphone.com]): 雷锋网 | 读懂智能，未来
 *  数据分析网 ([https://www.afenxi.com][https_www.afenxi.com]): 数据分析网 - 大数据学习交流第一平台
 *  知乎主题 ([https://www.zhihu.com/topic/19559450/hot][https_www.zhihu.com_topic_19559450_hot]): 知乎机器学习热门主题
 *  专知([http://www.zhuanzhi.ai/][http_www.zhuanzhi.ai])：专业可信的人工智能知识分发，包含5000+AI主题，荟萃、链路等资料

## 社区交流 ##

*  AIQ ([http://www.6aiq.com][http_www.6aiq.com] ): 机器学习大数据技术社区
 *  DataTau ([https://www.datatau.com][https_www.datatau.com]): 人工智能领域的Hacker News
 *  MathOverflow ([https://mathoverflow.net][https_mathoverflow.net]): 数学知识问答社区
 *  Medium ([https://medium.com/][https_medium.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
 *  专知([http://www.zhuanzhi.ai/][http_www.zhuanzhi.ai])：专业可信的人工智能知识分发，包含5000+AI主题，荟萃、链路等资料
 *  PaperWeekly ([http://www.paperweekly.site][http_www.paperweekly.site]): 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
 *  Quora ([https://www.quora.com/pinned/Machine-Learning][https_www.quora.com_pinned_Machine-Learning]): Quora | 机器学习主题
 *  Reddit ([https://www.reddit.com/r/MachineLearning][https_www.reddit.com_r_MachineLearning]): Reddit | 机器学习板块
 *  ShortScience ([http://www.shortscience.org][http_www.shortscience.org]): 用最简单的篇幅去概况科学著作
 *  SofaSofa ([http://sofasofa.io/index.php][http_sofasofa.io_index.php]): 做最好的数据科学社区
 *  Twitter ([https://twitter.com/StatMLPapers][https_twitter.com_StatMLPapers]): Twitter | 机器学习论文版块
 *  极智能 ([http://www.ziiai.com][http_www.ziiai.com]): 人工智能技术社区

## 优质博文 ##

*  Google AI Blog ([https://ai.googleblog.com/][https_ai.googleblog.com]): 谷歌AI博客
 *  handong1587 ([https://handong1587.github.io/][https_handong1587.github.io]): 深度学习各个方向资源汇总，及各大顶级会议/期刊资源
 *  Machine Learning Mastery ([https://machinelearningmastery.com/blog][https_machinelearningmastery.com_blog]): 帮助开发人员使用机器学习的知识解决复杂的问题
 *  Stats and Bots - Medium ([https://blog.statsbot.co][https_blog.statsbot.co]): 机器学习应用程序和代码的实用指南
 *  tornadomeet的博客 ([https://www.cnblogs.com/tornadomeet/archive/2012/06/24/2560261.html][https_www.cnblogs.com_tornadomeet_archive_2012_06_24_2560261.html]): 很详细的ML&DL学习博客
 *  爱可可-爱生活 ([https://weibo.com/fly51fly?topnav=1&wvr=6&topsug=1][https_weibo.com_fly51fly_topnav_1_wvr_6_topsug_1]): 知名互联网资讯博主
 *  超智能体 ([https://zhuanlan.zhihu.com/YJango][https_zhuanlan.zhihu.com_YJango]): 分享最通俗易懂的深度学习教程
 *  人工智能笔记 ([https://zhuanlan.zhihu.com/ainote][https_zhuanlan.zhihu.com_ainote]): 人工智能从入门到AI统治世界

## 论文检索 ##

*  arXiv ([https://arxiv.org][https_arxiv.org]): 康奈尔大学运营的学术预印本发布的平台
 *  Arxiv Sanity ([http://www.arxiv-sanity.com][http_www.arxiv-sanity.com]): 论文查询推荐
 *  GitXiv ([http://www.gitxiv.com][http_www.gitxiv.com]): arXiv的成果开源实现平台
 *  Papers with Code ([https://paperswithcode.com][https_paperswithcode.com]): 将论文与开源代码实现结合
 *  SCI-HUB ([https://sci-hub.tw/][https_sci-hub.tw]): 找论文必备
 *  猫咪论文 ([https://lunwen.im][https_lunwen.im]): 简单自由的论文下载平台

## 比赛实践 ##

*  Biendata ([https://biendata.com/][https_biendata.com])：数据科学竞赛平台
 *  DataCastle ([http://www.pkbigdata.com][http_www.pkbigdata.com]): 中国领先的数据科学竞赛平台
 *  DataFountain ([http://www.datafountain.cn/\#/][http_www.datafountain.cn]): DF,CCF指定专业大数据竞赛平台
 *  Kaggle ([https://www.kaggle.com][https_www.kaggle.com]): 为数据科学家提供举办机器学习竞赛
 *  KDD-CUP ([http://www.kdd.org/kdd-cup][http_www.kdd.org_kdd-cup]): 国际知识发现和数据挖掘竞赛
 *  滴滴新锐 ([http://research.xiaojukeji.com/trainee.html][http_research.xiaojukeji.com_trainee.html]): 滴滴面向全球高校博士、硕士、优秀本科生的精英人才计划
 *  JDD空间站 ([https://jdder.jd.com/][https_jdder.jd.com]): 京东算法赛事平台
 *  赛氪网 ([http://www.saikr.com][http_www.saikr.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
 *  天池大数据 ([https://tianchi.aliyun.com][https_tianchi.aliyun.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

# 资源 #

## 课程学习 ##

*  Data-science-complete-tutorial ([https://github.com/zekelabs/data-science-complete-tutorial][https_github.com_zekelabs_data-science-complete-tutorial]): 数据科学完整入门指南
 *  David Silver ([https://v.youku.com/v\_show/id\_XMjcwMDQyOTcxMg==.html?spm=a2h0j.11185381.listitem\_page1.5!4~A&&f=49376145][https_v.youku.com_v_show_id_XMjcwMDQyOTcxMg_.html_spm_a2h0j.11185381.listitem_page1.5_4_A_f_49376145])：David Silver 深度强化学习课程
 *  [fast.ai][] ([http://www.fast.ai/][http_www.fast.ai]): Making neural nets uncool again
 *  liuyubobobo ([https://coding.imooc.com/class/169.html][https_coding.imooc.com_class_169.html]): Python3 入门机器学习
 *  Metacademy ([https://metacademy.org/][https_metacademy.org]): 知识点检索并画出通向这个知识点的知识图谱
 *  Two Minute Papers ([https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg][https_www.youtube.com_channel_UCbfYPyITQ-7l4upoX8nvctg]): YouTube | 最简短的语言概况最新的热点论文
 *  3Blue1Brown ([https://www.youtube.com/channel/UCYO\_jab\_esuFRV4b17AJtAw][https_www.youtube.com_channel_UCYO_jab_esuFRV4b17AJtAw]): YouTube | 数学基础频道
 *  3Blue1Brown 中文 ([http://space.bilibili.com/88461692/\#/][http_space.bilibili.com_88461692]): Bilibili | 数学基础频道
 *  机器学习速成课程 ([https://developers.google.cn/machine-learning/crash-course/][https_developers.google.cn_machine-learning_crash-course]): Google制作的节奏紧凑、内容实用的机器学习简介课程
 *  林轩田 ([https://www.bilibili.com/video/av4294020/][https_www.bilibili.com_video_av4294020]): 机器学习基石
 *  林轩田 ([https://www.bilibili.com/video/av12469267][https_www.bilibili.com_video_av12469267]): 机器学习技法
 *  邱锡鹏（复旦大学）([https://github.com/nndl/nndl.github.io][https_github.com_nndl_nndl.github.io])：神经网络与深度学习
 *  吴恩达 ([http://study.163.com/course/introduction/1004570029.htm][http_study.163.com_course_introduction_1004570029.htm]): 机器学习课程
 *  吴恩达 ([https://mooc.study.163.com/smartSpec/detail/1001319001.htm][https_mooc.study.163.com_smartSpec_detail_1001319001.htm]): 深度学习课程

## 资源收集 ##

*  awesome-machine-learning-cn ([https://github.com/jobbole/awesome-machine-learning-cn][https_github.com_jobbole_awesome-machine-learning-cn]): 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
 *  Coursera-ML-AndrewNg-Notes ([https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes][https_github.com_fengdu78_Coursera-ML-AndrewNg-Notes]): 吴恩达老师的机器学习课程个人笔记
 *  daily-paper-computer-vision ([https://github.com/amusi/daily-paper-computer-vision][https_github.com_amusi_daily-paper-computer-vision]): 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
 *  deeplearning\_ai\_books ([https://github.com/fengdu78/deeplearning\_ai\_books][https_github.com_fengdu78_deeplearning_ai_books]): 吴恩达老师的深度学习课程笔记及资源
 *  Deep-Learning-Papers-Reading-Roadmap ([https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap][https_github.com_floodsung_Deep-Learning-Papers-Reading-Roadmap]): 深度学习论文阅读路线图
 *  Getting Started in Computer Vision Research ([https://sites.google.com/site/mostafasibrahim/research/articles/how-to-start][https_sites.google.com_site_mostafasibrahim_research_articles_how-to-start])：计算机视觉研究入门全指南
 *  lihang\_book\_algorithm ([https://github.com/WenDesi/lihang\_book\_algorithm][https_github.com_WenDesi_lihang_book_algorithm]): 《统计学习方法》算法python实现
 *  Machine Learning、Deep Learning([https://github.com/ty4z2008/Qix/blob/master/dl.md][https_github.com_ty4z2008_Qix_blob_master_dl.md]): ML&DL资料
 *  MachineLearning\_Python ([https://github.com/lawlite19/MachineLearning\_Python][https_github.com_lawlite19_MachineLearning_Python]): 机器学习算法python实现
 *  ml\_cheatsheet ([https://github.com/remicnrd/ml\_cheatsheet][https_github.com_remicnrd_ml_cheatsheet])：机器学习算法速查手册
 *  ml\_tutorials ([https://github.com/MorvanZhou/tutorials][https_github.com_MorvanZhou_tutorials]): 机器学习相关教程
 *  NLP-progress ([https://github.com/sebastianruder/NLP-progress][https_github.com_sebastianruder_NLP-progress])：跟踪NLP各项技术的state-of-the-art进展
 *  周志华 - 机器学习 ([https://github.com/Vay-keen/Machine-learning-learning-notes][https_github.com_Vay-keen_Machine-learning-learning-notes]): 周志华《机器学习》笔记

## 开源书籍 ##

*  deeplearningbook-chinese ([https://github.com/exacity/deeplearningbook-chinese][https_github.com_exacity_deeplearningbook-chinese]): 深度学习中文版
 *  deep\_learning\_cookbook ([https://github.com/DOsinga/deep\_learning\_cookbook][https_github.com_DOsinga_deep_learning_cookbook]): 深度学习手册
 *  hands\_on\_Ml\_with\_Sklearn\_and\_TF ([https://github.com/apachecn/hands\_on\_Ml\_with\_Sklearn\_and\_TF][https_github.com_apachecn_hands_on_Ml_with_Sklearn_and_TF]): Sklearn与TensorFlow机器学习实用指南
 *  Interpretable Machine Learning ([https://christophm.github.io/interpretable-ml-book/][https_christophm.github.io_interpretable-ml-book]): 一份指南，教你如何构建具有可解释性的黑盒模型
 *  Neural Networks and Deep Learning ([http://neuralnetworksanddeeplearning.com/index.html][http_neuralnetworksanddeeplearning.com_index.html]): 深度学习开源书籍
 *  Neural Networks and Deep Learning ([https://github.com/zhanggyb/nndl][https_github.com_zhanggyb_nndl]): 深度学习开源书籍 - 中文
 *  PythonDataScienceHandbook ([https://github.com/jakevdp/PythonDataScienceHandbook][https_github.com_jakevdp_PythonDataScienceHandbook]): Python数据科学手册
 *  TensorFlow-Course ([https://github.com/open-source-for-science/TensorFlow-Course][https_github.com_open-source-for-science_TensorFlow-Course]): 简单易学的TensorFlow教程
 *  机器学习实战 ([https://github.com/apachecn/MachineLearning][https_github.com_apachecn_MachineLearning]): Machine Learning in Action（机器学习实战）
 *  简单粗暴TensorFlow ([https://github.com/snowkylin/TensorFlow-cn][https_github.com_snowkylin_TensorFlow-cn]): 本手册是一篇精简的TensorFlow入门指导

## 实战项目 ##

*  face\_recognition ([https://github.com/ageitgey/face\_recognition][https_github.com_ageitgey_face_recognition]): 世界上最简单的人脸识别库
 *  style2paints ([https://github.com/lllyasviel/style2paints][https_github.com_lllyasviel_style2paints]): 线稿自动上色

# 文档 #

## Python ##

*  Caffe ([http://caffe.berkeleyvision.org/][http_caffe.berkeleyvision.org]): 一个基于表达式，速度和模块化原则创建的深度学习框架
 *  Caffe2 ([https://caffe2.ai/docs/getting-started.html?platform=windows&configuration=compile][https_caffe2.ai_docs_getting-started.html_platform_windows_configuration_compile]): Caffe2官方文档
 *  Chainer ([https://docs.chainer.org/en/stable/][https_docs.chainer.org_en_stable]): 基于Python的独立的深度学习模型开源框架
 *  CNTK ([https://docs.microsoft.com/en-us/cognitive-toolkit/][https_docs.microsoft.com_en-us_cognitive-toolkit]): CNTK官方文档
 *  Gensim ([https://radimrehurek.com/gensim/index.html][https_radimrehurek.com_gensim_index.html]): 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
 *  Keras ([https://keras.io/][https_keras.io]): Keras官方文档
 *  Matplotlib ([https://matplotlib.org/tutorials/index.html][https_matplotlib.org_tutorials_index.html]): Matplotlib官方文档
 *  MXNet ([http://mxnet.incubator.apache.org/tutorials/index.html][http_mxnet.incubator.apache.org_tutorials_index.html]): MXNet官方文档
 *  Neon ([http://neon.nervanasys.com/index.html/][http_neon.nervanasys.com_index.html]): Nervana公司一个基于Python的深度学习库
 *  NumPy ([http://www.numpy.org/][http_www.numpy.org]): NumPy官方文档
 *  pandas ([http://pandas.pydata.org/pandas-docs/stable/][http_pandas.pydata.org_pandas-docs_stable]): pandas官方文档
 *  PyBrain ([http://pybrain.org/docs/][http_pybrain.org_docs]): 一个模块化的Python机器学习库
 *  Pylearn2 ([http://deeplearning.net/software/pylearn2/][http_deeplearning.net_software_pylearn2]): 构建于Theano之上的机器学习库
 *  PyTorch ([https://pytorch.org/tutorials/][https_pytorch.org_tutorials]): PyTorch官方文档
 *  Seaborn ([https://seaborn.pydata.org/][https_seaborn.pydata.org]): Seaborn官方文档
 *  scikit-learn ([http://scikit-learn.org/stable/documentation.html][http_scikit-learn.org_stable_documentation.html]): scikit-learn官方文档
 *  Statsmodels ([http://www.statsmodels.org/stable/index.html][http_www.statsmodels.org_stable_index.html]): 用来探索数据，估计统计模型，进行统计测试
 *  TensorFlow ([https://www.tensorflow.org/tutorials/][https_www.tensorflow.org_tutorials]): TF官方文档
 *  Theano ([http://deeplearning.net/software/theano/][http_deeplearning.net_software_theano]): 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

## C & C++ ##

*  dlib ([http://dlib.net][http_dlib.net]): 实用的机器学习和数据分析工具包

## Java & Scala ##

*  DeepLearning4j ([https://deeplearning4j.org/][https_deeplearning4j.org]): 基于JAVA和Scala的商业级开源分布式深度学习框架

# 结束语 #

大家如果有好的学习网站或资源，欢迎留言并不断补充～

[https_github.com_howie6879_mlhub123]: https://github.com/howie6879/mlhub123
[https_www.analyticsvidhya.com_blog]: https://www.analyticsvidhya.com/blog/
[https_distill.pub]: https://distill.pub/
[https_news.google.com_topics_CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE_hl_en-US_gl_US_ceid_US_3Aen]: https://news.google.com/topics/CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE?hl=en-US&gl=US&ceid=US%3Aen
[http_news.mit.edu_topic_machine-learning]: http://news.mit.edu/topic/machine-learning
[http_www.17bigdata.com]: http://www.17bigdata.com
[https_www.jiqizhixin.com]: https://www.jiqizhixin.com
[https_www.leiphone.com]: https://www.leiphone.com
[https_www.afenxi.com]: https://www.afenxi.com
[https_www.zhihu.com_topic_19559450_hot]: https://www.zhihu.com/topic/19559450/hot
[http_www.zhuanzhi.ai]: http://www.zhuanzhi.ai/
[http_www.6aiq.com]: http://www.6aiq.com
[https_www.datatau.com]: https://www.datatau.com
[https_mathoverflow.net]: https://mathoverflow.net
[https_medium.com]: https://medium.com/
[http_www.paperweekly.site]: http://www.paperweekly.site
[https_www.quora.com_pinned_Machine-Learning]: https://www.quora.com/pinned/Machine-Learning
[https_www.reddit.com_r_MachineLearning]: https://www.reddit.com/r/MachineLearning
[http_www.shortscience.org]: http://www.shortscience.org
[http_sofasofa.io_index.php]: http://sofasofa.io/index.php
[https_twitter.com_StatMLPapers]: https://twitter.com/StatMLPapers
[http_www.ziiai.com]: http://www.ziiai.com
[https_ai.googleblog.com]: https://ai.googleblog.com/
[https_handong1587.github.io]: https://handong1587.github.io/
[https_machinelearningmastery.com_blog]: https://machinelearningmastery.com/blog
[https_blog.statsbot.co]: https://blog.statsbot.co
[https_www.cnblogs.com_tornadomeet_archive_2012_06_24_2560261.html]: https://www.cnblogs.com/tornadomeet/archive/2012/06/24/2560261.html
[https_weibo.com_fly51fly_topnav_1_wvr_6_topsug_1]: https://weibo.com/fly51fly?topnav=1&wvr=6&topsug=1
[https_zhuanlan.zhihu.com_YJango]: https://zhuanlan.zhihu.com/YJango
[https_zhuanlan.zhihu.com_ainote]: https://zhuanlan.zhihu.com/ainote
[https_arxiv.org]: https://arxiv.org
[http_www.arxiv-sanity.com]: http://www.arxiv-sanity.com
[http_www.gitxiv.com]: http://www.gitxiv.com
[https_paperswithcode.com]: https://paperswithcode.com
[https_sci-hub.tw]: https://sci-hub.tw/
[https_lunwen.im]: https://lunwen.im
[https_biendata.com]: https://biendata.com/
[http_www.pkbigdata.com]: http://www.pkbigdata.com
[http_www.datafountain.cn]: http://www.datafountain.cn/#/
[https_www.kaggle.com]: https://www.kaggle.com
[http_www.kdd.org_kdd-cup]: http://www.kdd.org/kdd-cup
[http_research.xiaojukeji.com_trainee.html]: http://research.xiaojukeji.com/trainee.html
[https_jdder.jd.com]: https://jdder.jd.com/
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