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一文打尽人工智能和机器学习网络资源

2018-03-02 13:30:57 0 人工智能 | , ,

公众号/大数据文摘

大数据文摘作品

编译:潇夜、大饼、蒋宝尚

昨天,谷歌刚刚上线的机器学习课程刷屏科技媒体头条(点击查看相关评测)。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。

  • Sebastian Thrun

    http://robots.stanford.edu

  • Yann Lecun

    http://yann.lecun.com

  • Nando de Freitas

    http://www.cs.ubc.ca/~nando/

  • Andrew Ng

    http://www.andrewng.org

  • Daphne Koller

    http://ai.stanford.edu/users/koller/

  • Adam Coates

    http://cs.stanford.edu/~acoates/

  • Jürgen Schmidhuber

    http://people.idsia.ch/~juergen/

  • 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/

  • 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。

  • 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

  • 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:

  • 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

  • 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

  • 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

  • Stanford CS224n — Natural Language Processing with Deep Learning (Winter 2017)

    https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6

  • Oxford Deep NLP 2017 (Phil Blunsom et al.)

    https://github.com/oxford-cs-deepnlp-2017/lectures

  • 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

  • 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。

  • sendex(22.5万订阅,2100万次观看)

    https://www.youtube.com/user/sentdex

  • Siraj Raval(14万订阅,500万次观看)

    https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A

  • 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

  • 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。

  • 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

  • Christopher Olah(推特关注数1.3万)

    http://colah.github.io

  • 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

  • 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/

  • 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

  • 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。

  • 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

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

  • 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

  • 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

  • 卷积神经网络 (2.5万关注)

    https://www.quora.com/topic/Convolutional-Neural-Networks-1?merged_tid=360493

  • 计算语言学(2.3万关注)

    https://www.quora.com/topic/Computational-Linguistics

  • 循环神经网络(1.74万关注)

    https://www.quora.com/topic/Recurrent-Neural-Networks-RNNs

Reddit

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。

  • /r/MachineLearning (11.1万订阅)

    https://www.reddit.com/r/MachineLearning

  • /r/robotics/ (4.3万订阅)

    https://www.reddit.com/r/robotics/

  • /r/artificial (3.5万订阅)

    https://www.reddit.com/r/artificial/

  • /r/datascience (3.4万订阅)

    https://www.reddit.com/r/datascience

  •  /r/learnmachinelearning (1.1万订阅)

    https://www.reddit.com/r/learnmachinelearning/

  • /r/computervision (1.1万订阅)

    https://www.reddit.com/r/computervision

  • /r/MLQuestions (8千订阅)

    https://www.reddit.com/r/MLQuestions

  • /r/LanguageTechnology (7千订阅)

    https://www.reddit.com/r/LanguageTechnology

  • /r/mlclass (4千订阅)

    https://www.reddit.com/r/mlclass

  • /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:

  • 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

  • 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

  • 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

  • O’Reilly Data Show

    http://radar.oreilly.com/tag/oreilly-data-show-podcast

  • Learning Machines 101

    http://www.learningmachines101.com

  • The Talking Machines

    http://www.thetalkingmachines.com

  • Artificial  Intelligence  in  Industry

    http://techemergence.com

  • Machine Learning Guide

    http://ocdevel.com/podcasts/machine-learning

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

  • 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

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

  • 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 Pattern Recognition)

    http://cvpr2017.thecvf.com

  • ICCF (International Conference on Computer Vision)

    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/

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。

  • 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

  • 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

  • Natural Language (6.2万条结果)

    https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false

  • Computer Vision (5.5万条结果)

    https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false

  • Deep Learning (2.4万条结果)

    https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false

  • 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~~~

原文链接:

https://unsupervisedmethods.com/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524

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