一文打尽人工智能和机器学习网络资源,反正我已经收藏了

  大数据文摘作品

  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

  Fran?ois 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。

  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。

  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。

  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~~~

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

  【今日机器学习概念】

  Have a Great Definition

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