机器学习秘籍:全网最详备的AI资源都在这里了

推荐会员: wanghua 所属分类: 行业精选 发布时间: 2017-08-10 11:46

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:

Home

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:

http://www.cs.toronto.edu/~hinton/

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:

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:

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:

Learning Machines 101: A Gentle Introduction to Artificial Intelligence and Machine Learning

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

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):

The Machine Learning Conference

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

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

翻译:AI科技大本营

关键词:

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