翻译 | AI科技大本营（rgznai100）
参与 | Joe，焦燕
2000年早期，Robbie Allen在写一本关于网络和编程的书的时候，深有感触。他发现，互联网很不错，但是资源并不完善。那时候，博客已经开始流行起来。但是，Youtube还不是很普遍，Quora、 Twitter和播客同样用者甚少。
在他转向人工智能和机器学习10年过后，局面发生了天翻地覆的变化：网上资源非相当丰富，以至于很多人出现了选择困难，不知道该从哪里开始（和停止）学习！
为了使大家能够更加便利地使用这些资源，Robbie Allen浏览查看各种各样的资源，把它们打包整理了出来。AI科技大本营在此借花献佛，和大家共同分享这些资源。通过它们，你将会对人工智能和机器学习有一个基本的认知。
这些资源内容安排如下：知名研究者，研究机构，视频课程，YouTube，博客，媒体作家，书籍，Quora主题栏，Reddit，Github库，播客， 实事通讯媒体、会议、论文。
如果你也有好的资源是这里没有列出的，欢迎评论区一起交流！
研究者
大多数知名的人工智能研究者在网络上的曝光率还是很高的。下面列举了20位知名学者，以及他们的个人网站链接，维基百科链接，推特主页，Google学术主页，Quora主页。他们中相当一部分人在Reddit或Quora上面参与了问答。
Sebastian Thrun
个人官网：
https://robots.stanford.edu/
Wikipedia：
https://en.wikipedia.org/wiki/Sebastian_Thrun
Twitter：
https://twitter.com/SebastianThrun
Google Scholar：
https://scholar.google.com/citations?user=7K34d7cAAAAJ&hl=en&oi=ao
Quora：
https://www.quora.com/profile/Sebastian-Thrun
Reddit AMA：
https://www.reddit.com/r/IAmA/comments/v59z3/iam_sebastian_thrun_stanford_professor_google_x/
Yann LeCun
个人官网：
https://yann.lecun.com/
Wikipedia：
https://en.wikipedia.org/wiki/Sebastian_Thrun
Twitter：
https://twitter.com/ylecun?
Google Scholar：
https://scholar.google.com/citations?user=WLN3QrAAAAAJ&hl=en
Quora：
https://www.quora.com/profile/Yann-LeCun
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/3y4zai/ama_nando_de_freitas/
Nando de Freitas
个人官网：
https://www.cs.ubc.ca/~nando/
Wikipedia：
https://en.wikipedia.org/wiki/Nando_de_Freitas
Twitter：
https://twitter.com/NandoDF
Google Scholar：
https://scholar.google.com/citations?user=nzEluBwAAAAJ&hl=en
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/3y4zai/ama_nando_de_freitas/
Andrew Ng
个人官网：
https://www.andrewng.org/
Wikipedia：
https://en.wikipedia.org/wiki/Andrew_Ng
Twitter：
https://twitter.com/AndrewYNg
Google Scholar：
https://scholar.google.com/citations?use
Quora：
https://www.quora.com/profile/Andrew-Ng"
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/
Daphne Koller
个人官网：
https://ai.stanford.edu/users/koller/
Wikipedia：
https://en.wikipedia.org/wiki/Daphne_Koller
Twitter：
https://twitter.com/DaphneKoller?lang=en
Google Scholar：
https://scholar.google.com/citations?user=5Iqe53IAAAAJ
Quora：
https://www.quora.com/profile/Daphne-Koller
Quora Session：
https://www.quora.com/session/Daphne-Koller/1
Adam Coates
个人官网：
https://cs.stanford.edu/~acoates/
Twitter：
https://twitter.com/adampaulcoates
Google Scholar：
https://scholar.google.com/citations?user=bLUllHEAAAAJ&hl=en"
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/
Jürgen Schmidhuber
个人官网：
https://people.idsia.ch/~juergen/
Wikipedia：
https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber
Google Scholar：
https://scholar.google.com/citations?user=gLnCTgIAAAAJ&hl=en
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am_j%C3%BCrgen_schmidhuber_ama/
Geoffrey Hinton
个人官网：
Wikipedia：
https://en.wikipedia.org/wiki/Geoffrey_Hinton
Google Scholar：
https://www.cs.toronto.edu/~hinton/
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/
Terry Sejnowski
个人官网：
https://www.salk.edu/scientist/terrence-sejnowski/
Wikipedia：
https://en.wikipedia.org/wiki/Terry_Sejnowski
Twitter：
https://twitter.com/sejnowski?lang=en
Google Scholar：
https://scholar.google.com/citations?user=m1qAiOUAAAAJ&hl=en
Reddit AMA：
https://www.reddit.com/r/IAmA/comments/2id4xd/we_are_barb_oakley_terry_sejnowski_instructors_of/
Michael Jordan
个人官网：
https://people.eecs.berkeley.edu/~jordan/
Wikipedia：
https://en.wikipedia.org/wiki/Michael_I._Jordan
Google Scholar：
https://scholar.google.com/citations?user=yxUduqMAAAAJ&hl=en"
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/
Peter Norvig
个人官网：
https://norvig.com/
Wikipedia：
https://en.wikipedia.org/wiki/Peter_Norvig
Google Scholar：
https://scholar.google.com/citations?user=Ol0vcWgAAAAJ&hl=en
Reddit AMA：
https://www.reddit.com/r/blog/comments/b8aln/peter_norvig_answers_your_questions_ask_me/
Yoshua Bengio
个人官网：
https://www.iro.umontreal.ca/~bengioy/yoshua_en/
Wikipedia：
https://en.wikipedia.org/wiki/Yoshua_Bengio
Google Scholar：
https://scholar.google.com/citations?user=kukA0LcAAAAJ&hl=en
Quora：
https://www.quora.com/profile/Yoshua-Bengio
Reddit AMA：
https://www.reddit.com/r/MachineLearning/comments/1ysry1/ama_yoshua_bengio/
Ina Goodfellow
个人官网：
https://www.iangoodfellow.com/
Wikipedia：
https://en.wikipedia.org/wiki/Ian_Goodfellow
Twitter：
https://twitter.com/goodfellow_ian
Google Scholar：
https://scholar.google.com/citations?user=iYN86KEAAAAJ&hl=en
Quora：
https://www.quora.com/profile/Ian-Goodfellow
Quora Session：
https://www.quora.com/session/Ian-Goodfellow/1
Andrej Karpathy
个人官网：
https://karpathy.github.io/
Twitter：
https://twitter.com/karpathy
Google Scholar：
https://scholar.google.com/citations?user=l8WuQJgAAAAJ&hl=en
Quora：
https://www.quora.com/profile/Andrej-Karpathy
Quora Session：
https://www.quora.com/session/Andrej-Karpathy/1
Richard Socher
个人官网：
https://www.socher.org/
Twitter：
https://twitter.com/RichardSocher
Google Scholar：
https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en
Interview：
https://www.kdnuggets.com/2015/10/metamind-mastermind-richard-socher-deep-learning-interview.html
Demis Hassabis
个人官网：
https://demishassabis.com/
Wikipedia：
https://en.wikipedia.org/wiki/Demis_Hassabis
Twitter：
https://twitter.com/demishassabis
Google Scholar：
https://scholar.google.com/citations?user=dYpPMQEAAAAJ&hl=en
Interview：
https://www.bloomberg.com/features/2016-demis-hassabis-interview-issue/
Christopher Manning
个人官网：
https://nlp.stanford.edu/~manning/
Twitter：
https://twitter.com/chrmanning
Google Scholar：
https://scholar.google.com/citations?user=1zmDOdwAAAAJ&hl=en"
Fei-Fei Li
个人官网：
https://vision.stanford.edu/people.html
Wikipedia：
https://en.wikipedia.org/wiki/Fei-Fei_Li
Twitter：
https://twitter.com/drfeifei
Google Scholar：
https://scholar.google.com/citations?user=1zmDOdwAAAAJ&hl=en"
Ted Talk：
https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures/tran?language=en
François Chollet
个人官网：
https://scholar.google.com/citations?user=VfYhf2wAAAAJ&hl=en
Twitter：
https://twitter.com/fchollet
Google Scholar：
https://scholar.google.com/citations?user=VfYhf2wAAAAJ&hl=en
Quora：
https://www.quora.com/profile/Fran%C3%A7ois-Chollet
Quora Session：
https://www.quora.com/session/Fran%C3%A7ois-Chollet/1
Dan Jurafsky
个人官网：
https://web.stanford.edu/~jurafsky/
Wikipedia：
https://en.wikipedia.org/wiki/Daniel_Jurafsky
Twitter：
https://twitter.com/jurafsky
Google Scholar：
https://scholar.google.com/citations?user=uZg9l58AAAAJ&hl=en
Oren Etzioni
个人官网：
https://allenai.org/team/orene/
Wikipedia：
https://en.wikipedia.org/wiki/Oren_Etzioni
Twitter：
https://twitter.com/etzioni
Google Scholar：
https://scholar.google.com/citations?user=XF6Yk98AAAAJ&hl=en
Quora：
https://scholar.google.com/citations?user
Reddit AMA：
https://www.reddit.com/r/IAmA/comments/2hdc09/im_oren_etzioni_head_of_paul_allens_institute_for/
机构
网络上有大量的知名机构致力于推进人工智能领域的研究和发展。
以下列出的是同时拥有官方网站/博客和推特账号的机构。
OpenAI
官网：https://openai.com/
Twitter：https://twitter.com/OpenAI
DeepMind
官网：https://deepmind.com/
Twitter：https://twitter.com/DeepMindA
Google Research
官网：https://research.googleblog.com/
Twitter：https://twitter.com/googleresearch
AWS AI
官网：https://aws.amazon.com/blogs/ai/
Twitter：https://twitter.com/awscloud
Facebook AI Research
官网：https://research.fb.com/category/facebook-ai-research-fair/
Microsoft Research
官网：https://www.microsoft.com/en-us/research/
Twitter：https://twitter.com/MSFTResearch
Baidu Research
官网：https://research.baidu.com/
Twitter：https://twitter.com/baiduresearch?lang=en
IntelAI
官网：https://software.intel.com/en-us/ai
Twitter：https://twitter.com/IntelAI
AI2
官网：https://allenai.org/
Twitter：https://twitter.com/allenai_org
Partnership on AI
官网：https://www.partnershiponai.org/
Twitter：https://twitter.com/partnershipai
视频课程
以下列出的是一些免费的视频课程和教程。
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
Udacity — Intro to Machine Learning (Sebastian Thrun)：
https://classroom.udacity.com/courses/ud120
Udacity — Machine Learning (Georgia Tech)：
https://www.udacity.com/course/machine-learning--ud262
Udacity — Deep Learning (Vincent Vanhoucke)：
https://www.udacity.com/course/deep-learning--ud730
Machine Learning (mathematicalmonk)：
https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA
Practical Deep Learning For Coders (Jeremy Howard & Rachel Thomas)：
https://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
(class link)：https://cs231n.stanford.edu/
Stanford CS224n — Natural Language Processing with Deep Learning (Winter 2017) ：
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
(class link)：https://web.stanford.edu/class/cs224n/
Oxford Deep NLP 2017 (Phil Blunsom et al.)：
https://github.com/oxford-cs-deepnlp-2017/lectures
Reinforcement Learning (David Silver)：
https://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
YouTube
以下，我列举了一些YoutTube频道和用户，它们的主要内容是人工智能或者机器学习。这里按照受欢迎程度列举如下：
sentdex (225K subscribers, 21M views)：
https://www.youtube.com/user/sentdex
Artificial Intelligence A.I. (7M views)：
https://www.youtube.com/channel/UC-XbFeFFzNbAUENC8Ofpn3g
Siraj Raval (140K subscribers, 5M views)：
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
Two Minute Papers (60K subscribers, 3.3M views)：
https://www.youtube.com/user/keeroyz
DeepLearning.TV (42K subscribers, 1.7M views)：
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
Data School (37K subscribers, 1.8M views)：
https://www.youtube.com/user/dataschool
Machine Learning Recipes with Josh Gordon (324K views)：
https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal
Artificial Intelligence — Topic (10K subscribers)：
https://www.youtube.com/channel/UC9pXDvrYYsHuDkauM2fLllQ
Allen Institute for Artificial Intelligence (AI2) (1.6K subscribers, 69K views)：
https://www.youtube.com/channel/UCEqgmyWChwvt6MFGGlmUQCQ
Machine Learning at Berkeley (634 subscribers, 48K views)：
https://www.youtube.com/channel/UCXweTmAk9K-Uo9R6SmfGtjg
Understanding Machine Learning — Shai Ben-David (973 subscribers, 43K views)：
https://www.youtube.com/channel/UCR4_akQ1HYMUcDszPQ6jh8Q
Machine Learning TV (455 subscribers, 11K views)：
https://www.youtube.com/channel/UChIaUcs3tho6XhyU6K6KMrw
博客
Andrej Karpathy
博客：https://karpathy.github.io/
Twitter：https://twitter.com/karpathy
i am trask
博客：https://iamtrask.github.io/
Twitter：https://twitter.com/iamtrask
Christopher Olah
博客：https://colah.github.io/
Twitter：https://twitter.com/ch402
Top Bots
博客：https://www.topbots.com/
Twitter：https://twitter.com/topbots
WildML
博客：https://www.wildml.com/
Twitter：https://twitter.com/dennybritz
Distill
博客：https://distill.pub/
Twitter：https://twitter.com/distillpub
Machine Learning Mastery
博客：https://machinelearningmastery.com/blog/
Twitter：https://twitter.com/TeachTheMachine
FastML
博客：https://fastml.com/
Twitter：https://twitter.com/fastml_extra
Adventures in NI
博客：https://joanna-bryson.blogspot.de/
Twitter：https://twitter.com/j2bryson
Sebastian Ruder
博客：https://sebastianruder.com/
Twitter：https://twitter.com/seb_ruder
Unsupervised Methods
博客：https://unsupervisedmethods.com/
Twitter：https://twitter.com/RobbieAllen
Explosion
博客：https://explosion.ai/blog/
Twitter：https://twitter.com/explosion_ai
Tim Dettwers
博客：https://timdettmers.com/
Twitter：https://twitter.com/Tim_Dettmers
When trees fall...
博客：https://blog.wtf.sg/
Twitter：https://twitter.com/tanshawn
ML@B
博客：https://ml.berkeley.edu/blog/
Twitter：https://twitter.com/berkeleyml
媒体作家
以下是一些人工智能领域方向顶尖的媒体作家。
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
以下列出的是关于机器学习、深度学习和自然语言处理的书。这些书都是免费的，可以通过网络获取或者下载。
机器学习
Understanding Machine Learning From Theory to Algorithms：
https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
Machine Learning Yearning：
https://www.mlyearning.org/
A Course in Machine Learning：
https://ciml.info/
Machine Learning：
https://www.intechopen.com/books/machine_learning
Neural Networks and Deep Learning：
https://neuralnetworksanddeeplearning.com/
Deep Learning Book：
https://www.deeplearningbook.org/
Reinforcement Learning: An Introduction：
https://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：
https://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：
https://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：
https://greenteapress.com/wp/think-stats-2e/
The Probability and Statistics Cookbook：
https://statistics.zone/
Linear Algebra：
https://joshua.smcvt.edu/linearalgebra/book.pdf
Linear Algebra Done Wrong：
https://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：
https://www.math.lmu.de/~philip/publications/lectureNotes/calc1_forInfAndStatStudents.pdf
Quora
Quora对于人工智能和机器学习来说是一个非常好的资源。许多业界最顶尖的研究者会对Quora上某些问题进行回答。以下，我列举了主要的人工智能相关的主题，你可以订阅如果你想跟进这些内容。
Computer-Science (5.6M followers)：
https://www.quora.com/topic/Computer-Science
Machine-Learning (1.1M followers)：
https://www.quora.com/topic/Machine-Learning
Artificial-Intelligence (635K followers)：
https://www.quora.com/topic/Artificial-Intelligence
Deep-Learning (167K followers)：
https://www.quora.com/topic/Deep-Learning
Natural-Language-Processing (155K followers)：
https://www.quora.com/topic/Natural-Language-Processing
Classification-machine-learning (119K followers)：
https://www.quora.com/topic/Classification-machine-learning
Artificial-General-Intelligence (82K followers)
https://www.quora.com/topic/Artificial-General-Intelligence
Convolutional-Neural-Networks-CNNs (25K followers)：
https://www.quora.com/topic/Artificial-General-Intelligence
Computational-Linguistics (23K followers)：
https://www.quora.com/topic/Computational-Linguistics
Recurrent-Neural-Networks (17.4K followers)：
https://www.quora.com/topic/Recurrent-Neural-Networks
Reddit上的人工智能社区并没有Quora上的那么大，但是，Reddit上面依然有一些值得关注的资源。Reddit有助于跟进最新的业界动态和研究进展，而Quora便于进行问答交流。以下通过关注量列举了主要的人工智能领域的subreddits。
/r/MachineLearning (111K readers)：
https://www.reddit.com/r/MachineLearning
/r/robotics/ (43K readers)：
https://www.reddit.com/r/robotics/
/r/artificial (35K readers)：
https://www.reddit.com/r/artificial
/r/datascience (34K readers)：
https://www.reddit.com/r/datascience
/r/learnmachinelearning (11K readers)：
https://www.reddit.com/r/learnmachinelearning
/r/computervision (11K readers)：
https://www.reddit.com/r/computervision
/r/MLQuestions (8K readers)：
https://www.reddit.com/r/MLQuestions
/r/LanguageTechnology (7K readers)：
https://www.reddit.com/r/LanguageTechnology
/r/mlclass (4K readers)：
https://www.reddit.com/r/mlclass
/r/mlpapers (4K readers)：
https://www.reddit.com/r/mlpapers
Github
人工智能领域最令人激动的原因之一是大多数项目都是开源的，而且可以通过Github获得。如果你需要一些Python或Jupyter Notebooks实现的示例算法，在Github上有大量的这类教育资源。
Machine Learning (6K repos)：
https://github.com/search?o=desc&q=topic%3Amachine-learning+&s=stars&type=Repositories&utf8=%E2%9C%93
Deep Learning (3K repos)：
https://github.com/search?q=topic%3Adeep-learning&type=Repositories
Tensorflow (2K repos)：
https://github.com/search?q=topic%3Atensorflow&type=Repositories
Neural Network (1K repos)：
https://github.com/search?q=topic%3Atensorflow&type=Repositories
NLP (1K repos)：
https://github.com/search?utf8=%E2%9C%93&q=topic%3Anlp&type=Repositories
播客
对人工智能进行报道的播客数量在不断地增加，一部分关注最新的动态，一部分关注人工智能教育。
ConcerningAI
官网：
https://concerning.ai/
iTunes:
https://itunes.apple.com/us/podcast/concerning-ai-artificial-intelligence/id1038719211
This Week in Machine Learning and AI
官网：
https://twimlai.com/
iTunes：
https://itunes.apple.com/us/podcast/this-week-in-machine-learning/id1116303051?mt=2
The AI Podcast
官网：
https://blogs.nvidia.com/ai-podcast/
iTunes：
https://itunes.apple.com/us/podcast/the-ai-podcast/id1186480811
Data Skeptic
官网：
https://dataskeptic.com/
iTunes：
https://itunes.apple.com/us/podcast/the-data-skeptic-podcast/id890348705
Linear Digressions
官网：
https://itunes.apple.com/us/podcast/linear-digressions/id941219323
iTunes：
https://itunes.apple.com/us/podcast/linear-digressions/id941219323?mt=2
Partially Dervative
官网：
https://partiallyderivative.com/
iTunes：
https://itunes.apple.com/us/podcast/partially-derivative/id942048597?mt=2
O'Reilly Data Show
官网：
https://radar.oreilly.com/tag/oreilly-data-show-podcast
iTunes：
https://itunes.apple.com/us/podcast/oreilly-data-show/id944929220
Learning Machines 101
官网：
https://www.learningmachines101.com/
iTunes：
https://itunes.apple.com/us/podcast/learning-machines-101/id892779679?mt=2
The Talking Machines
官网：
https://www.thetalkingmachines.com/
iTunes：
https://itunes.apple.com/us/podcast/talking-machines/id955198749?mt=2
Artificial Intelligence in Industry
官网：
https://techemergence.com/
iTunes：
https://itunes.apple.com/us/podcast/artificial-intelligence-in-industry-with-dan-faggella/id670771965?mt=2
Machine Learning Guide
官网
https://ocdevel.com/podcasts/machine-learning
https://itunes.apple...iTunes：
https://itunes.apple.com/us/podcast/machine-learning-guide/id1204521130?mt=2
时事通讯媒体
如果你想了解最新的业界消息和学术进展，这里有大量的时事通讯媒体供你选择。
The Exponential View：
https://www.getrevue.co/profile/azeem
AI Weekly：
https://aiweekly.co/
Deep Hunt：
https://deephunt.in/
O’Reilly Artificial Intelligence Newsletter：
https://www.oreilly.com/ai/newsletter.html
Machine Learning Weekly：
https://mlweekly.com/
Data Science Weekly Newsletter：
https://www.datascienceweekly.org/
Machine Learnings：
https://subscribe.machinelearnings.co/
Artificial Intelligence News：
https://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：
https://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：
https://www.deeplearningweekly.com/
Data Science Weekly：
https://www.datascienceweekly.org/
KDnuggets Newsletter：
https://www.kdnuggets.com/news/subscribe.html?qst
会议
随着人工智能的崛起，与人工智能相关的会议也在逐渐增加。这里列举一些主要的会议。
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)：
https://www.kdd.org/
ICLR (International Conference on Learning Representations)：
https://www.iclr.cc/
ACL (Association for Computational Linguistics)：
https://acl2017.org/
EMNLP (Empirical Methods in Natural Language Processing)：
https://emnlp2017.net/
CVPR (Computer Vision and PatternRecognition)：
https://cvpr2017.thecvf.com/
ICCF(InternationalConferenceonComputerVision)：
https://iccv2017.thecvf.com/
专业会议
O’Reilly Artificial Intelligence Conference：
https://conferences.oreilly.com/artificial-intelligence/
Machine Learning Conference (MLConf)：
https://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/
论文
arXiv.org上特定领域论文集：
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搜索结果：
Neural Networks (179K results)：
https://www.semanticscholar.org/search?q=%22neural%20networks%22&sort=relevance&ae=false
Machine Learning (94K results)：
https://www.semanticscholar.org/search?q=%22machine%20learning%22&sort=relevance&ae=false
Natural Language (62K results)：
https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false
Computer Vision (55K results)：
https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false
Deep Learning (24K results)：
https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false
此外，一个很好的资源是Andrej Karpathy维护的一个用于搜索论文的项目。
https://www.arxiv-sanity.com/
作者：Robbie Allen
原文：https://unsupervisedmethods.com/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524
人类感知外界信息，80%以上通过视觉得到。2015年，微软在ImageNet大赛中，算法识别率首次超越人类，视觉由此成为人工智能最为活跃的领域。为此，AI100特邀哈尔滨工业大学副教授、视觉技术研究室负责人屈老师，为大家介绍计算机视觉原理及实战。扫描上图二维码或加微信csdn02，了解更多课程信息。