全面掌握人工智能与机器学习：网络资源大汇总

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