一文打尽人工智能和机器学习网络资源，反正我已经收藏了！

2022-11-09 19:17:41 服务器 ℃

大数据文摘作品编译：潇夜、大饼、蒋宝尚昨天，谷歌刚刚上线的机器学习课程刷屏科技媒体头条（点击查看相关评测）。激动过后，多数AI学习者会陷入焦虑：入坑人工智能，到底要从何入手？的确，如今学习人工智能最大的困难不是找不到资料，更多同学的痛苦是：网上资源太多了，以至于没法知道从哪儿开始搜索，也没法知道搜到什么程度。为了节省大家的时间，我们搜遍网络把最好的免费资源汇总整理到这篇文章当中。这些链接够你学上很久，而且你看完本文一定会再次惊叹：现在网上关于机器学习、深度学习和人工智能的信息真的非常多。本文罗列了以下几个方面的学习资源，供大家收藏：知名研究人员、人工智能研究机构、视频课程、博客、Medium、书籍、YouTube、Quora、Reddit、GitHub、播客、新闻订阅、科研会议、研究论文链接、教程以及各种小抄表。研究人员许多著名的人工智能研究人员都在网络上有很强的影响力。下面我列出了20个专家，也给出了能够找到他们详细信息的网站。 Sebastian Thrun http://robots.stanford.edu Yann Lecun http://yann.lecun.com Nando de Freitas http://www.cs.ubc.ca/~nando/ Andrew Ng http://www.andrewng.org Daphne Koller http://ai.stanford.edu/users/koller/ Adam Coates http://cs.stanford.edu/~acoates/ Jürgen Schmidhuber http://people.idsia.ch/~juergen/ Geoffrey Hinton http://www.cs.toronto.edu/~hinton/ Terry Sejnowski http://www.salk.edu/scientist/terrence-sejnowski/ Michael Jordan https://people.eecs.berkeley.edu/~jordan/ Peter Norvig http://norvig.com Yoshua Bengio http://www.iro.umontreal.ca/~bengioy/yoshua_en/ Ian Goodfellow http://www.iangoodfellow.com Andrej Karpathy http://karpathy.github.io Richard Socher http://www.socher.org Demis Hassabis http://demishassabis.com Christopher Manning https://nlp.stanford.edu/~manning/ Fei-Fei Li http://vision.stanford.edu/people.html François Chollet https://scholar.google.com/citations?user=VfYhf2wAAAAJ&hl=en Larry Carin http://people.ee.duke.edu/~lcarin/ Dan Jurafsky https://web.stanford.edu/~jurafsky/ Oren Etzioni http://allenai.org/team/orene/ 人工智能研究机构许多研究机构致力于促进人工智能的研究与开发。下面我列出了一些机构的网站。 OpenAI（推特数12.7万） https://openai.com DeepMind（推特数8万） https://deepmind.com Google Research（推特数110万） https://research.googleblog.com AWS AI（推特数140万） https://aws.amazon.com/blogs/ai/ Facebook AI Research https://research.fb.com/category/facebook-ai-research-fair/ Microsoft Research（推特数34.1万） https://www.microsoft.com/en-us/research/ Baidu Research（推特数1.8万） http://research.baidu.com IntelAI（推特数2千） https://software.intel.com/en-us/ai-academy AI²（推特数4.6千） http://allenai.org Partnership on AI（推特数5千） https://www.partnershiponai.org 视频课程网上也有大量的视频课程和教程，其中很多都是免费的，还有一些付费的也很不错，但是在这篇文章中我只提供免费内容的链接。下面我列出的这些免费课程可以让你学上好几个月： Coursera — Machine Learning (Andrew Ng) https://www.coursera.org/learn/machine-learning#syllabus Coursera — Neural Networks for Machine Learning (Geoffrey Hinton) https://www.coursera.org/learn/neural-networks Machine Learning (mathematicalmonk) https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA Practical Deep Learning For Coders (Jeremy Howard & Rachel Thomas) http://course.fast.ai/start.html Stanford CS231n — Convolutional Neural Networks for Visual Recognition (Winter 2022) https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA 斯坦福CS231n【中字】视频，大数据文摘经授权翻译 http://study.163.com/course/introduction/1003223001.htm Stanford CS224n — Natural Language Processing with Deep Learning (Winter 2022) https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6 Oxford Deep NLP 2022 (Phil Blunsom et al.) https://github.com/oxford-cs-deepnlp-2022/lectures 牛津Deep NLP【中字】视频，大数据文摘经授权翻译 http://study.163.com/course/introduction/1004336028.htm Reinforcement Learning (David Silver) http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html Practical Machine Learning Tutorial with Python (sentdex) https://www.youtube.com/watch?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&v=OGxgnH8y2NM 油管 YouTube YouTube上有很多频道或者用户都经常会发布一些AI或者机器学习相关的内容，我把这些链接按照订阅数/观看数多少列示在下边，这样方便看出来哪个更受欢迎。 sex（22.5万订阅，2100万次观看） https://www.youtube.com/user/sentdex Siraj Raval（14万订阅，500万次观看） https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A Two Minute Papers（6万订阅，330万次观看） https://www.youtube.com/user/keeroyz DeepLearning.TV（4.2万订阅，140万观看） https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ Data School（3.7万订阅，180万次观看） https://www.youtube.com/user/dataschool Machine Learning Recipes with Josh Gordon（32.4万次观看） https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal Artificial Intelligence — Topic（1万订阅） https://www.youtube.com/channel/UC9pXDvrYYsHuDkauM2fLllQ Allen Institute for Artificial Intelligence (AI2)（1.6千订阅，6.9万次观看） https://www.youtube.com/channel/UCEqgmyWChwvt6MFGGlmUQCQ Machine Learning at Berkeley（634订阅，4.8万次观看） https://www.youtube.com/channel/UCXweTmAk9K-Uo9R6SmfGtjg Understanding Machine Learning — Shai Ben-David（973订阅，4.3万次观看） https://www.youtube.com/channel/UCR4_akQ1HYMUcDszPQ6jh8Q Machine Learning TV（455订阅，1.1万次观看） https://www.youtube.com/channel/UChIaUcs3tho6XhyU6K6KMrw 博客虽然人工智能和机器学习现在这么火，但是我很惊讶地发现相关博主并没有那么多。可能是因为内容比较复杂，把有意义的部分整理出来需要花很大精力；也有可能是因为类似Quora这样的平台比较多，专家们回答问题更方便也不需要花太多时间做详细论述。下面我会按照推特的数排序介绍一些博主，他们一直在做人工智能相关的原创内容，而不只是一些新闻摘要或者公司博客。 Andrej Karpathy（推特数6.9万） http://karpathy.github.io i am trask（推特数1.4万） http://iamtrask.github.io Christopher Olah（推特数1.3万） http://colah.github.io Top Bots（推特数1.1万） http://www.topbots.com WildML（推特数1万） http://www.wildml.com Distill（推特数9千） https://distill.pub Machine Learning Mastery（推特数5千） http://machinelearningmastery.com/blog/ FastML（推特数5千） http://fastml.com Adventures in NI（推特数5千） https://joanna-bryson.blogspot.de Sebastian Ruder（推特数3千） http://sebastianruder.com Unsupervised Methods（推特数1.7千） http://unsupervisedmethods.com Explosion（推特数1千） https://explosion.ai/blog/ Tim Dettmers（推特数1千） http://timdettmers.com When trees fall…（推特数265） http://blog.wtf.sg ML@B（推特数80） https://ml.berkeley.edu/blog/ Medium平台上的作者下面介绍到的是Medium上人工智能相关的顶级作者，按照2022年Mediumas的排行榜排序。 Robbie Allen https://medium.com/@robbieallen Erik P.M. Vermeulen https://medium.com/@erikpmvermeulen Frank Chen https://medium.com/@withfries2 azeem https://medium.com/@azeem Sam DeBrule https://medium.com/@samdebrule Derrick Harris https://medium.com/@derrickharris Yitaek Hwang https://medium.com/@yitaek samim https://medium.com/@samim Paul Boutin https://medium.com/@Paul_Boutin Mariya Yao https://medium.com/@thinkmariya Rob May https://medium.com/@robmay Avinash Hindupur https://medium.com/@hindupuravinash 书籍市面上有许多关于机器学习、深度学习和自然语言处理等方面的书籍，我只列示了可以直接从网上免费获得或者下载的书籍。机器学习 Understanding Machine Learning From Theory to Algorithms http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Machine Learning Yearning http://www.mlyearning.org A Course in Machine Learning http://ciml.info Machine Learning https://www.intechopen.com/books/machine_learning Neural Networks and Deep Learning http://neuralnetworksanddeeplearning.com Deep Learning Book http://www.deeplearningbook.org Reinforcement Learning: An Introduction http://incompleteideas.net/sutton/book/the-book-2nd.html Reinforcement Learning https://www.intechopen.com/books/reinforcement_learning 自然语言处理 Speech and Language Processing (3rd ed. draft) https://web.stanford.edu/~jurafsky/slp3/ Natural Language Processing with Python http://www.nltk.org/book/ An Introduction to Information Retrieval https://nlp.stanford.edu/IR-book/html/htmledition/irbook.html 数学 Introduction to Statistical Thought http://people.math.umass.edu/~lavine/Book/book.pdf Introduction to Bayesian Statistics https://www.stat.auckland.ac.nz/~brewer/stats331.pdf Introduction to Probability https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf Think Stats: Probability and Statistics for Python programmers http://greenteapress.com/wp/think-stats-2e/ The Probability and Statistics Cookbook http://statistics.zone Linear Algebra http://joshua.smcvt.edu/linearalgebra/book.pdf Linear Algebra Done Wrong http://www.math.brown.edu/~treil/papers/LADW/book.pdf Linear Algebra, Theory And Applications https://math.byu.edu/~klkuttle/Linearalgebra.pdf Mathematics for Computer Science https://courses.csail.mit.edu/6.042/spring17/mcs.pdf Calculus https://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf Calculus I for Computer Science and Statistics Students http://www.math.lmu.de/~philip/publications/lectureNotes/calc1_forInfAndStatStudents.pdf Quora Quora已经成为人工智能和机器学习的重要资源，许多顶尖的研究人员会在上面回答问题。下面我列出了一些主要关于人工智能的话题，如果你想自定义你的Quora喜好，你可以选择订阅这些话题。记得去查看每个话题下的FAQ部分（例如机器学习下常见问题解答），你可以看到Quora社区里提供的一些常见问题列表。计算机科学 (560万) https://www.quora.com/topic/Computer-Science 机器学习 (110万) https://www.quora.com/topic/Machine-Learning 人工智能 (63.5万) https://www.quora.com/topic/Artificial-Intelligence 深度学习 (16.7万) https://www.quora.com/topic/Deep-Learning 自然语言处理 (15.5 万) https://www.quora.com/topic/Natural-Language-Processing 机器学习分类(11.9万) https://www.quora.com/topic/Classification-machine-learning 通用人工智能(8.2万 ) https://www.quora.com/topic/Artificial-General-Intelligence 卷积神经网络 (2.5万) https://www.quora.com/topic/Convolutional-Neural-Networks-1?merged_tid=360493 计算语言学(2.3万) https://www.quora.com/topic/Computational-Linguistics 循环神经网络(1.74万) https://www.quora.com/topic/Recurrent-Neural-Networks-RNNs Reddit Reddit上的人工智能社区并没有Quora上那么活跃，但是还是有一些很不错的话题。相对于Quora问答的形式，Reddit更适合于用来跟踪最新的新闻和研究。下面是一些主要关于人工智能的Reddit话题，按照订阅人数排序。 /r/MachineLearning (11.1万订阅) https://www.reddit.com/r/MachineLearning /r/robotics/ (4.3万订阅) https://www.reddit.com/r/robotics/ /r/artificial (3.5万订阅) https://www.reddit.com/r/artificial/ /r/datascience (3.4万订阅) https://www.reddit.com/r/datascience /r/learnmachinelearning (1.1万订阅) https://www.reddit.com/r/learnmachinelearning/ /r/computervision (1.1万订阅) https://www.reddit.com/r/computervision /r/MLQuestions (8千订阅) https://www.reddit.com/r/MLQuestions /r/LanguageTechnology (7千订阅) https://www.reddit.com/r/LanguageTechnology /r/mlclass (4千订阅) https://www.reddit.com/r/mlclass /r/mlpapers (4千订阅) https://www.reddit.com/r/mlpapers Github 人工智能社区的好处之一是大部分新项目都是开源的，并且能在GitHub上获取到。同样如果你想了解使用Python或者Juypter Notebooks来实现实例算法，GitHub上也有很多学习资源可以帮助到你。以下是一些GitHub项目：机器学习(6千个项目) https://github.com/search?o=desc&q=topic%3Amachine-learning+&s=stars&type=Repositories&utf8=✓ 深度学习(3千个项目) https://github.com/search?q=topic%3Adeep-learning&type=Repositories Tensorflow (2千个项目) https://github.com/search?q=topic%3Atensorflow&type=Repositories 神经网络(1千个项目) https://github.com/search?q=topic%3Aneural-network&type=Repositories 自然语言处理(1千个项目) https://github.com/search?utf8=✓&q=topic%3Anlp&type=Repositories 播客人工智能相关的播客数量在不断的增加，有些播客最新的新闻，有些教授相关知识。 Concerning AI https://concerning.ai his Week in Machine Learning and AI https://twimlai.com The AI Podcast https://blogs.nvidia.com/ai-podcast/ Data Skeptic http://dataskeptic.com Linear Digressions https://itunes.apple.com/us/podcast/linear-digressions/id941219323 Partially Derivative http://partiallyderivative.com O’Reilly Data Show http://radar.oreilly.com/tag/oreilly-data-show-podcast Learning Machines 101 http://www.learningmachines101.com The Talking Machines http://www.thetalkingmachines.com Artificial Intelligence in Industry http://techemergence.com Machine Learning Guide http://ocdevel.com/podcasts/machine-learning 新闻订阅如果你想追踪最新的新闻和研究的话，种类渐增的每周新闻是一个不错的选择：其中大部分都包含相同的内容，所以订阅两三个就足够。 The Exponential View https://www.getrevue.co/profile/azeem AI Weekly http://aiweekly.co Deep Hunt https://deephunt.in O’Reilly Artificial Intelligence Newsletter http://www.oreilly.com/ai/newsletter.html Machine Learning Weekly http://mlweekly.com Data Science Weekly Newsletter https://www.datascienceweekly.org Machine Learnings http://subscribe.machinelearnings.co Artificial Intelligence News http://aiweekly.co When trees fall… https://meetnucleus.com/p/GVBR82UWhWb9 WildML https://meetnucleus.com/p/PoZVx95N9RGV Inside AI https://inside.com/technically-sentient Kurzweil AI http://www.kurzweilai.net/create-account Import AI https://jack-clark.net/import-ai/ The Wild Week in AI https://www.getrevue.co/profile/wildml Deep Learning Weekly http://www.deeplearningweekly.com Data Science Weekly https://www.datascienceweekly.org KDnuggets Newsletter http://www.kdnuggets.com/news/subscribe.html?qst 科研会议随着人工智能的普及，人工智能相关的科研会议数量也在不断增加。我只提了几个主要的会议，没列所有的。（当然会议并不是免费的！）学术会议 NIPS (Neural Information Processing Systems) https://nips.cc ICML (International Conference on Machine Learning) https://2022.icml.cc KDD (Knowledge Discovery and Data Mining) http://www.kdd.org ICLR (International Conference on Learning Representations) http://www.iclr.cc ACL (Association for Computational Linguistics) http://acl2022.org EMNLP (Empirical Methods in Natural Language Processing) http://emnlp2022.net CVPR (Computer Vision and Pattern Recognition) http://cvpr2022.thecvf.com ICCF (International Conference on Computer Vision) http://iccv2022.thecvf.com 专业会议 O’Reilly Artificial Intelligence Conference https://conferences.oreilly.com/artificial-intelligence/ Machine Learning Conference (MLConf) http://mlconf.com AI Expo (North America, Europe, World) https://www.ai-expo.net AI Summit https://theaisummit.com AI Conference https://aiconference.ticketleap.com/helloworld/ 研究论文你可以在网上浏览或者搜索已经发布的学术论文。 arXiv.org的主题类别 arXiv 是较早的预印本库，也是物理学及相关专业领域中最大的，该数据库目前已有数学、物理学和计算机科学方面的论文可开放获取的达50多万篇。 Artificial Intelligence https://arxiv.org/list/cs.AI/recent Learning (Computer Science) https://arxiv.org/list/cs.LG/recent Machine Learning (Stats) https://arxiv.org/list/stat.ML/recent NLP https://arxiv.org/list/cs.CL/recent Computer Vision https://arxiv.org/list/cs.CV/recent Semantic Scholar内搜索 Semantic Scholar是由微软联合创始人保罗·艾伦创立的艾伦人工智能研究所推出的学术搜索引擎 Neural Networks (17.9万条结果) https://www.semanticscholar.org/search?q=%22neural%20networks%22&sort=relevance&ae=false Machine Learning (9.4万条结果) https://www.semanticscholar.org/search?q=%22machine%20learning%22&sort=relevance&ae=false Natural Language (6.2万条结果) https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false Computer Vision (5.5万条结果) https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false Deep Learning (2.4万条结果) https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false Andrej Karpathy开发的网站 http://www.arxiv-sanity.com/ 教程我另外单独有一篇详细的文章涵盖了我发现的所有的优秀教程内容：超过150种最佳的机器学习、自然语言处理和Python教程 https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd7 小抄表和教程一样，我同样单独有一篇文章介绍了许多种很有用的小抄表：机器学习、Python和数学小抄表 https://unsupervisedmethods.com/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6 通读完本篇文章，是不是对于如何查找关于人工智能领域的资料有了清晰的方向。资料很多，大多都是国外的网站，所以大家需要科学上网哟~~~ 原文链接： https://unsupervisedmethods.com/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524