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。
资源目录：
□ 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
□ Github库
□ 播客
□ 实事通讯媒体
□ 会议
□ 论文
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
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
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/
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