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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!

木马童年 2019-3-22 12:35 354 0

编译:潇夜、大饼、蒋宝尚 近日,谷歌刚刚上线的机器学习课程刷屏科技媒体头条。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 ...

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!

编译:潇夜、大饼、蒋宝尚

近日,谷歌刚刚上线的机器学习课程刷屏科技媒体头条。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

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!

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
  • Franois 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

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!

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
  • AI(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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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

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!

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
  • ICCV (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

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!

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

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!

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

在不久的将来,多智时代一定会彻底走入我们的生活,有兴趣入行未来前沿产业的朋友,可以收藏多智时代,及时获取人工智能、大数据、云计算和物联网的前沿资讯和基础知识,让我们一起携手,引领人工智能的未来!

机器学习 人工智能 深度学习 大数据 自然语言处理 计算机科学
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