昨天，谷歌剛剛上線的機器學習課程刷屏科技媒體頭條。激動過後，多數AI學習者會陷入焦慮：入坑人工智能，到底要從何入手？
的确，如今學習人工智能最大的困難不是找不到資料，更多同學的痛苦是：網上資源太多了，以至于沒法知道從哪兒開始搜尋，也沒法知道搜到什麼程度。
為了節省大家的時間，我們搜遍網絡把最好的免費資源彙總整理到這篇文章當中。這些連結夠你學上很久，而且你看完本文一定會再次驚歎：現在網上關于機器學習、深度學習和人工智能的資訊真的非常多。
本文羅列了以下幾個方面的學習資源，供大家收藏：知名研究人員、人工智能研究機構、視訊課程、部落格、Medium、書籍、YouTube、Quora、Reddit、GitHub、播客、新聞訂閱、科研會議、研究論文連結、教程以及各種小抄表。
研究人員
許多著名的人工智能研究人員都在網絡上有很強的影響力。下面我列出了20個專家，也給出了能夠找到他們詳細資訊的網站。
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/
人工智能研究機構
許多研究機構緻力于促進人工智能的研究與開發。下面我列出了一些機構的網站。
OpenAI（推特關注數12.7萬）
https://openai.com
DeepMind（推特關注數8萬）
https://deepmind.com
Google Research（推特關注數110萬）
https://research.googleblog.com
AWS AI（推特關注數140萬）
https://aws.amazon.com/blogs/ai/
Facebook AI Research
https://research.fb.com/category/facebook-ai-research-fair/
Microsoft Research（推特關注數34.1萬）
https://www.microsoft.com/en-us/research/
Baidu Research（推特關注數1.8萬）
http://research.baidu.com
IntelAI（推特關注數2千）
https://software.intel.com/en-us/ai-academy
AI²（推特關注數4.6千）
http://allenai.org
Partnership on AI（推特關注數5千）
https://www.partnershiponai.org
視訊課程
網上也有大量的視訊課程和教程，其中很多都是免費的，還有一些付費的也很不錯，但是在這篇文章中我隻提供免費内容的連結。下面我列出的這些免費課程可以讓你學上好幾個月：
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】視訊，大資料文摘經授權翻譯
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】視訊，大資料文摘經授權翻譯
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上有很多頻道或者使用者都經常會釋出一些AI或者機器學習相關的内容，我把這些連結按照訂閱數/觀看數多少列示在下邊，這樣友善看出來哪個更受歡迎。
sendex（22.5萬訂閱，2100萬次觀看）
https://www.youtube.com/user/sentdex
Siraj Raval（14萬訂閱，500萬次觀看）
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
Two Minute Papers（6萬訂閱，330萬次觀看）
https://www.youtube.com/user/keeroyz
DeepLearning.TV（4.2萬訂閱，140萬觀看）
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
Data School（3.7萬訂閱，180萬次觀看）
https://www.youtube.com/user/dataschool
Machine Learning Recipes with Josh Gordon（32.4萬次觀看）
https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal
Artificial Intelligence — Topic（1萬訂閱）
https://www.youtube.com/channel/UC9pXDvrYYsHuDkauM2fLllQ
Allen Institute for Artificial Intelligence (AI2)（1.6千訂閱，6.9萬次觀看）
https://www.youtube.com/channel/UCEqgmyWChwvt6MFGGlmUQCQ
Machine Learning at Berkeley（634訂閱，4.8萬次觀看）
https://www.youtube.com/channel/UCXweTmAk9K-Uo9R6SmfGtjg
Understanding Machine Learning — Shai Ben-David（973訂閱，4.3萬次觀看）
https://www.youtube.com/channel/UCR4_akQ1HYMUcDszPQ6jh8Q
Machine Learning TV（455訂閱，1.1萬次觀看）
https://www.youtube.com/channel/UChIaUcs3tho6XhyU6K6KMrw
部落格
雖然人工智能和機器學習現在這麼火，但是我很驚訝地發現相關部落客并沒有那麼多。可能是因為内容比較複雜，把有意義的部分整理出來需要花很大精力；也有可能是因為類似Quora這樣的平台比較多，專家們回答問題更友善也不需要花太多時間做詳細論述。
下面我會按照推特的關注數排序介紹一些部落客，他們一直在做人工智能相關的原創内容，而不隻是一些新聞摘要或者公司部落格。
Andrej Karpathy（推特關注數6.9萬）
i am trask（推特關注數1.4萬）
http://iamtrask.github.io
Christopher Olah（推特關注數1.3萬）
http://colah.github.io
Top Bots（推特關注數1.1萬）
http://www.topbots.com
WildML（推特關注數1萬）
http://www.wildml.com
Distill（推特關注數9千）
https://distill.pub
Machine Learning Mastery（推特關注數5千）
http://machinelearningmastery.com/blog/
FastML（推特關注數5千）
http://fastml.com
Adventures in NI（推特關注數5千）
https://joanna-bryson.blogspot.de
Sebastian Ruder（推特關注數3千）
http://sebastianruder.com
Unsupervised Methods（推特關注數1.7千）
http://unsupervisedmethods.com
Explosion（推特關注數1千）
https://explosion.ai/blog/
Tim Dettmers（推特關注數1千）
http://timdettmers.com
When trees fall…（推特關注數265）
http://blog.wtf.sg
ML@B（推特關注數80）
https://ml.berkeley.edu/blog/
Medium平台上的作者
下面介紹到的是Medium上人工智能相關的頂級作者，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
書籍
市面上有許多關于機器學習、深度學習和自然語言處理等方面的書籍，我隻列示了可以直接從網上免費獲得或者下載下傳的書籍。
機器學習
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
自然語言處理
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
數學
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已經成為人工智能和機器學習的重要資源，許多頂尖的研究人員會在上面回答問題。下面我列出了一些主要關于人工智能的話題，如果你想自定義你的Quora喜好，你可以選擇訂閱這些話題。記得去檢視每個話題下的FAQ部分（例如機器學習下常見問題解答），你可以看到Quora社群裡提供的一些常見問題清單。
計算機科學 (560萬關注)
https://www.quora.com/topic/Computer-Science
機器學習 (110萬關注)
https://www.quora.com/topic/Machine-Learning
人工智能 (63.5萬關注)
https://www.quora.com/topic/Artificial-Intelligence
深度學習 (16.7萬關注)
https://www.quora.com/topic/Deep-Learning
自然語言處理 (15.5 萬關注)
https://www.quora.com/topic/Natural-Language-Processing
機器學習分類(11.9萬關注)
https://www.quora.com/topic/Classification-machine-learning
通用人工智能(8.2萬 關注)
https://www.quora.com/topic/Artificial-General-Intelligence
卷積神經網絡 (2.5萬關注)
https://www.quora.com/topic/Convolutional-Neural-Networks-1?merged_tid=360493
計算語言學(2.3萬關注)
https://www.quora.com/topic/Computational-Linguistics
循環神經網絡(1.74萬關注)
https://www.quora.com/topic/Recurrent-Neural-Networks-RNNs
Reddit上的人工智能社群并沒有Quora上那麼活躍，但是還是有一些很不錯的話題。相對于Quora問答的形式，Reddit更适合于用來跟蹤最新的新聞和研究。下面是一些主要關于人工智能的Reddit話題，按照訂閱人數排序。
/r/MachineLearning (11.1萬訂閱)
https://www.reddit.com/r/MachineLearning
/r/robotics/ (4.3萬訂閱)
https://www.reddit.com/r/robotics/
/r/artificial (3.5萬訂閱)
https://www.reddit.com/r/artificial/
/r/datascience (3.4萬訂閱)
https://www.reddit.com/r/datascience
/r/learnmachinelearning (1.1萬訂閱)
https://www.reddit.com/r/learnmachinelearning/
/r/computervision (1.1萬訂閱)
https://www.reddit.com/r/computervision
/r/MLQuestions (8千訂閱)
https://www.reddit.com/r/MLQuestions
/r/LanguageTechnology (7千訂閱)
https://www.reddit.com/r/LanguageTechnology
/r/mlclass (4千訂閱)
https://www.reddit.com/r/mlclass
/r/mlpapers (4千訂閱)
https://www.reddit.com/r/mlpapers
Github
人工智能社群的好處之一是大部分新項目都是開源的，并且能在GitHub上擷取到。同樣如果你想了解使用Python或者Juypter Notebooks來實作執行個體算法，GitHub上也有很多學習資源可以幫助到你。以下是一些GitHub項目：
機器學習(6千個項目)
https://github.com/search?o=desc&q=topic%3Amachine-learning+&s=stars&type=Repositories&utf8=
深度學習(3千個項目)
https://github.com/search?q=topic%3Adeep-learning&type=Repositories
Tensorflow (2千個項目)
https://github.com/search?q=topic%3Atensorflow&type=Repositories
神經網絡(1千個項目)
https://github.com/search?q=topic%3Aneural-network&type=Repositories
自然語言處理(1千個項目)
https://github.com/search?utf8=&q=topic%3Anlp&type=Repositories
播客
人工智能相關的播客數量在不斷的增加，有些播客關注最新的新聞，有些關注教授相關知識。
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-learnin
新聞訂閱
如果你想追蹤最新的新聞和研究的話，種類漸增的每周新聞是一個不錯的選擇：其中大部分都包含相同的内容，是以訂閱兩三個就足夠。
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
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
KDnuggets Newsletter
http://www.kdnuggets.com/news/subscribe.html?qst
科研會議
随着人工智能的普及，人工智能相關的科研會議數量也在不斷增加。我隻提了幾個主要的會議，沒列所有的。（當然會議并不是免費的！）
學術會議
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
專業會議
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/
研究論文
你可以在網上浏覽或者搜尋已經釋出的學術論文。
arXiv.org的主題類别
arXiv 是較早的預印本庫，也是實體學及相關專業領域中最大的，該資料庫目前已有數學、實體學和計算機科學方面的論文可開放擷取的達50多萬篇。
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内搜尋
Semantic Scholar是由微軟聯合創始人保羅·艾倫創立的艾倫人工智能研究所推出的學術搜尋引擎
Neural Networks (17.9萬條結果)
https://www.semanticscholar.org/search?q=%22neural%20networks%22&sort=relevance&ae=false
Machine Learning (9.4萬條結果)
https://www.semanticscholar.org/search?q=%22machine%20learning%22&sort=relevance&ae=false
Natural Language (6.2萬條結果)
https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false
Computer Vision (5.5萬條結果)
https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false
Deep Learning (2.4萬條結果)
https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false
Andrej Karpathy開發的網站
http://www.arxiv-sanity.com/
教程
我另外單獨有一篇詳細的文章涵蓋了我發現的所有的優秀教程内容：
超過150種最佳的機器學習、自然語言處理和Python教程
https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd7
小抄表
和教程一樣，我同樣單獨有一篇文章介紹了許多種很有用的小抄表：
機器學習、Python和數學小抄表
https://unsupervisedmethods.com/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
通讀完本篇文章，是不是對于如何查找關于人工智能領域的資料有了清晰的方向。資料很多，大多都是國外的網站，是以大家需要科學上網喲~~~
原文釋出時間為：2018-03-12