昨天，谷歌刚刚上线的机器学习课程刷屏科技媒体头条。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（推特关注数12.7万）
https://openai.com
DeepMind（推特关注数8万）
https://deepmind.com
Google Research（推特关注数110万）
https://research.googleblog.com
AWS AI（推特关注数140万）
https://aws.amazon.com/blogs/ai/
Facebook AI Research
https://research.fb.com/category/facebook-ai-research-fair/
Microsoft Research（推特关注数34.1万）
https://www.microsoft.com/en-us/research/
Baidu Research（推特关注数1.8万）
http://research.baidu.com
IntelAI（推特关注数2千）
https://software.intel.com/en-us/ai-academy
AI²（推特关注数4.6千）
http://allenai.org
Partnership on AI（推特关注数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 2016)
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 2017)
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
Oxford Deep NLP 2017 (Phil Blunsom et al.)
https://github.com/oxford-cs-deepnlp-2017/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。
sendex（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关注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（推特关注数6.9万）
i am trask（推特关注数1.4万）
http://iamtrask.github.io
Christopher Olah（推特关注数1.3万）
http://colah.github.io
Top Bots（推特关注数1.1万）
http://www.topbots.com
WildML（推特关注数1万）
http://www.wildml.com
Distill（推特关注数9千）
https://distill.pub
Machine Learning Mastery（推特关注数5千）
http://machinelearningmastery.com/blog/
FastML（推特关注数5千）
http://fastml.com
Adventures in NI（推特关注数5千）
https://joanna-bryson.blogspot.de
Sebastian Ruder（推特关注数3千）
http://sebastianruder.com
Unsupervised Methods（推特关注数1.7千）
http://unsupervisedmethods.com
Explosion（推特关注数1千）
https://explosion.ai/blog/
Tim Dettmers（推特关注数1千）
http://timdettmers.com
When trees fall…（推特关注数265）
http://blog.wtf.sg
ML@B（推特关注数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2017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 (560万关注)
https://www.quora.com/topic/Computer-Science
机器学习 (110万关注)
https://www.quora.com/topic/Machine-Learning
人工智能 (63.5万关注)
https://www.quora.com/topic/Artificial-Intelligence
深度学习 (16.7万关注)
https://www.quora.com/topic/Deep-Learning
自然语言处理 (15.5 万关注)
https://www.quora.com/topic/Natural-Language-Processing
机器学习分类(11.9万关注)
https://www.quora.com/topic/Classification-machine-learning
通用人工智能(8.2万 关注)
https://www.quora.com/topic/Artificial-General-Intelligence
卷积神经网络 (2.5万关注)
https://www.quora.com/topic/Convolutional-Neural-Networks-1?merged_tid=360493
计算语言学(2.3万关注)
https://www.quora.com/topic/Computational-Linguistics
循环神经网络(1.74万关注)
https://www.quora.com/topic/Recurrent-Neural-Networks-RNNs
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，有些播客关注最新的新闻，有些关注教授相关知识。
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-learnin
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
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
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://2017.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://acl2017.org
EMNLP (Empirical Methods in Natural Language Processing)
http://emnlp2017.net
CVPR (Computer Vision and Pattern Recognition)
http://cvpr2017.thecvf.com
ICCV (International Conference on Computer Vision)
http://iccv2017.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~~~
原文发布时间为：2018-03-12