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。
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Sebastian Thrun
http://robots.stanford.edu
-
Yann Lecun
http://yann.lecun.com
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Nando de Freitas
http://www.cs.ubc.ca/~nando/
-
Andrew Ng
http://www.andrewng.org
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Daphne Koller
http://ai.stanford.edu/users/koller/
-
Adam Coates
http://cs.stanford.edu/~acoates/
-
Jürgen Schmidhuber
http://people.idsia.ch/~juergen/
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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/
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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。
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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
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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:
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Coursera — Machine Learning (Andrew Ng)
https://www.coursera.org/learn/machine-learning#syllabus
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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
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Practical Deep Learning For Coders (Jeremy Howard & Rachel Thomas)
http://course.fast.ai/start.html
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Stanford CS231n — Convolutional Neural Networks for Visual Recognition (Winter 2016)
https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA
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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
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Stanford CS224n — Natural Language Processing with Deep Learning (Winter 2017)
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
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Oxford Deep NLP 2017 (Phil Blunsom et al.)
https://github.com/oxford-cs-deepnlp-2017/lectures
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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
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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。
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sendex(22.5万订阅,2100万次观看)
https://www.youtube.com/user/sentdex
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Siraj Raval(14万订阅,500万次观看)
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
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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
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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。
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Andrej Karpathy(推特关注数6.9万)
http://karpathy.github.io
-
i am trask(推特关注数1.4万)
http://iamtrask.github.io
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Christopher Olah(推特关注数1.3万)
http://colah.github.io
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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
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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/
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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
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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。
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Robbie Allen
https://medium.com/@robbieallen
-
Erik P.M. Vermeulen
https://medium.com/@erikpmvermeulen
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Frank Chen
https://medium.com/@withfries2
-
azeem
https://medium.com/@azeem
-
Sam DeBrule
https://medium.com/@samdebrule
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Derrick Harris
https://medium.com/@derrickharris
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Yitaek Hwang
https://medium.com/@yitaek
-
samim
https://medium.com/@samim
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Paul Boutin
https://medium.com/@Paul_Boutin
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Mariya Yao
https://medium.com/@thinkmariya
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Rob May
https://medium.com/@robmay
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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
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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
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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
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Reinforcement Learning: An Introduction
http://incompleteideas.net/sutton/book/the-book-2nd.html
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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/
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Natural Language Processing with Python
http://www.nltk.org/book/
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An Introduction to Information Retrieval
https://nlp.stanford.edu/IR-book/html/htmledition/irbook.html
数学
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Introduction to Statistical Thought
http://people.math.umass.edu/~lavine/Book/book.pdf
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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
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Think Stats: Probability and Statistics for Python programmers
http://greenteapress.com/wp/think-stats-2e/
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The Probability and Statistics Cookbook
http://statistics.zone
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Linear Algebra
http://joshua.smcvt.edu/linearalgebra/book.pdf
-
Linear Algebra Done Wrong
http://www.math.brown.edu/~treil/papers/LADW/book.pdf
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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
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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
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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
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卷积神经网络 (2.5万关注)
https://www.quora.com/topic/Convolutional-Neural-Networks-1?merged_tid=360493
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计算语言学(2.3万关注)
https://www.quora.com/topic/Computational-Linguistics
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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。
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/r/MachineLearning (11.1万订阅)
https://www.reddit.com/r/MachineLearning
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/r/robotics/ (4.3万订阅)
https://www.reddit.com/r/robotics/
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/r/artificial (3.5万订阅)
https://www.reddit.com/r/artificial/
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/r/datascience (3.4万订阅)
https://www.reddit.com/r/datascience
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/r/learnmachinelearning (1.1万订阅)
https://www.reddit.com/r/learnmachinelearning/
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/r/computervision (1.1万订阅)
https://www.reddit.com/r/computervision
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/r/MLQuestions (8千订阅)
https://www.reddit.com/r/MLQuestions
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/r/LanguageTechnology (7千订阅)
https://www.reddit.com/r/LanguageTechnology
-
/r/mlclass (4千订阅)
https://www.reddit.com/r/mlclass
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/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:
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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
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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
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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
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O’Reilly Data Show
http://radar.oreilly.com/tag/oreilly-data-show-podcast
-
Learning Machines 101
http://www.learningmachines101.com
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The Talking Machines
http://www.thetalkingmachines.com
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Artificial Intelligence in Industry
http://techemergence.com
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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
http://aiweekly.co
-
When trees fall…
https://meetnucleus.com/p/GVBR82UWhWb9
-
WildML
https://meetnucleus.com/p/PoZVx95N9RGV
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Inside AI
https://inside.com/technically-sentient
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Kurzweil AI
http://www.kurzweilai.net/create-account
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Import AI
https://jack-clark.net/import-ai/
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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
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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
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CVPR (Computer Vision and Pattern Recognition)
http://cvpr2017.thecvf.com
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ICCV (International Conference on Computer Vision)
http://iccv2017.thecvf.com
专业会议
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O’Reilly Artificial Intelligence Conference
https://conferences.oreilly.com/artificial-intelligence/
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Machine Learning Conference (MLConf)
http://mlconf.com
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AI Expo (North America, Europe, World)
https://www.ai-expo.net
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AI Summit
https://theaisummit.com
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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。
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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
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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
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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
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Natural Language (6.2万条结果)
https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false
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Computer Vision (5.5万条结果)
https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false
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Deep Learning (2.4万条结果)
https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false
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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