葵花宝典之机器学习：全网最重要的AI资源都在这里了（大牛，研究机构，视频，博客，书籍，Quora......）

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： http://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： http://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： http://www.reddit.com/r/MachineLearning/comments/3y4zai/ama_nando_de_freitas/

Nando de Freitas

个人官网： http://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： http://www.reddit.com/r/MachineLearning/comments/3y4zai/ama_nando_de_freitas/

Andrew Ng

个人官网： http://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： http://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/

Daphne Koller

个人官网： http://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： http://cs.stanford.edu/~acoates/ Twitter： https://twitter.com/adampaulcoates Google Scholar： https://scholar.google.com/citations?user=bLUllHEAAAAJ&hl=en" Reddit AMA： http://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/

Jürgen Schmidhuber

个人官网： http://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： http://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： http://www.cs.toronto.edu/~hinton/ Reddit AMA： http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/

Terry Sejnowski

个人官网： http://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： http://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/

Peter Norvig

个人官网： http://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： http://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： http://www.reddit.com/r/MachineLearning/comments/1ysry1/ama_yoshua_bengio/

Ina Goodfellow

个人官网： http://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： http://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： http://www.socher.org/ Twitter： https://twitter.com/RichardSocher Google Scholar： https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en Interview： http://www.kdnuggets.com/2015/10/metamind-mastermind-richard-socher-deep-learning-interview.html

Demis Hassabis

个人官网： http://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： http://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/transcript?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： http://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：http://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：http://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)： 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 (class link)：http://cs231n.stanford.edu/ Stanford CS224n — Natural Language Processing with Deep Learning (Winter 2017) ： https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6 (class link)：http://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)： 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

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：http://karpathy.github.io/ Twitter：https://twitter.com/karpathy

i am trask

博客：http://iamtrask.github.io/ Twitter：https://twitter.com/iamtrask

Christopher Olah

博客：http://colah.github.io/ Twitter：https://twitter.com/ch402

Top Bots

博客：http://www.topbots.com/ Twitter：https://twitter.com/topbots

WildML

博客：http://www.wildml.com/ Twitter：https://twitter.com/dennybritz

Distill

博客：http://distill.pub/ Twitter：https://twitter.com/distillpub

Machine Learning Mastery

博客：http://machinelearningmastery.com/blog/ Twitter：https://twitter.com/TeachTheMachine

FastML

博客：http://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：http://sebastianruder.com/ Twitter：https://twitter.com/seb_ruder

Unsupervised Methods

博客：http://unsupervisedmethods.com/ Twitter：https://twitter.com/RobbieAllen

Explosion

博客：https://explosion.ai/blog/ Twitter：https://twitter.com/explosion_ai

Tim Dettwers

博客：http://timdettmers.com/ Twitter：https://twitter.com/Tim_Dettmers

When trees fall...

博客：http://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： 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

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： http://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： http://partiallyderivative.com/ iTunes： https://itunes.apple.com/us/podcast/partially-derivative/id942048597?mt=2

O'Reilly Data Show

官网： http://radar.oreilly.com/tag/oreilly-data-show-podcast iTunes： https://itunes.apple.com/us/podcast/oreilly-data-show/id944929220

Learning Machines 101

官网： http://www.learningmachines101.com/ iTunes： https://itunes.apple.com/us/podcast/learning-machines-101/id892779679?mt=2

The Talking Machines

官网： http://www.thetalkingmachines.com/ iTunes： https://itunes.apple.com/us/podcast/talking-machines/id955198749?mt=2

Artificial Intelligence in Industry

官网：

http://techemergence.com/ iTunes： https://itunes.apple.com/us/podcast/artificial-intelligence-in-industry-with-dan-faggella/id670771965?mt=2

Machine Learning Guide

官网 http://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： 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

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)： 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 PatternRecognition)： http://cvpr2017.thecvf.com/ ICCF(InternationalConferenceonComputerVision)： 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/

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。

http://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