本部分資源內容主要是國外的一些AI學習與開發內容，包括AI組織，視頻課程，博客，書籍，YouTube頻道，Quora，Github，書籍推薦，會議，研究鏈接，教程等。
以下內容由公衆號：AIRX社區（國內領先的AI、AR、VR技術學習與交流平臺） 整理
組織機構
有一些著名的組織致力於推動AI研究與開發。
1、OpenAI
https://openai.com/
2、DeepMind
https://deepmind.com/
3、Google Research
https://research.googleblog.com/
4、AWS AI
https://aws.amazon.com/blogs/ai/
5、微軟研究院
https://www.microsoft.com/en-us/research/
6、Facebook AI研究
https://research.fb.com/category/facebook-ai-research-fair/
7、百度研究
http://research.baidu.com/
8、IntelAI
https://software.intel.com/en-us/ai
9、AI²
http://allenai.org/
10、AI
https://www.partnershiponai.org/
視頻課程
現在網上有大量的視頻課程和教程，其中很多都是免費的，也有一些不錯的付費選擇，但在本文中，我只列舉一些免費內容。
1、Coursera-機器學習
https://www.coursera.org/learn/machine-learning#syllabus
2、Coursera —機器學習的神經網絡
https://www.coursera.org/learn/neural-networks
3、Udacity —機器學習入門
https://classroom.udacity.com/courses/ud120
4、Udacity —機器學習
https://www.udacity.com/course/machine-learning--ud262
5、Udacity —深度學習
https://www.udacity.com/course/deep-learning--ud730
6、機器學習
https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA
7、面向程序員的實用深度學習
http://course.fast.ai/start.html
8、Stanford—用於視覺識別的卷積神經網絡
https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA
9、Stanford—具有深度學習的自然語言處理
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
10、牛津大學深層自然語言處理課程
https://github.com/oxford-cs-deepnlp-2017/lectures
11、Python實用機器學習教程
https://www.youtube.com/watch?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&v=OGxgnH8y2NM
Youtube精選
下面提供了一些YouTube頻道或用戶的鏈接，這些頻道或用戶具有與AI或機器學習相關的常規內容。
1、sentdex (225K subscribers, 21M views)
https://www.youtube.com/user/sentdex
2、Siraj Raval (140K subscribers, 5M views)
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
3、Two Minute Papers (60K subscribers, 3.3M views)
https://www.youtube.com/user/keeroyz
4、DeepLearning.TV (42K subscribers, 1.7M views)
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
5、Data School (37K subscribers, 1.8M views)
https://www.youtube.com/user/dataschool
6、Machine Learning Recipes with Josh Gordon (324K views)
https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal
7、Artificial Intelligence — Topic (10K subscribers)
https://www.youtube.com/channel/UC9pXDvrYYsHuDkauM2fLllQ
8、Allen Institute for Artificial Intelligence (AI2) (1.6K subscribers, 69K views)
https://www.youtube.com/channel/UCEqgmyWChwvt6MFGGlmUQCQ
9、Machine Learning at Berkeley (634 subscribers, 48K views)
https://www.youtube.com/channel/UCXweTmAk9K-Uo9R6SmfGtjg
10、Understanding Machine Learning — Shai Ben-David (973 subscribers, 43K views)
https://www.youtube.com/channel/UCR4_akQ1HYMUcDszPQ6jh8Q
11、Machine Learning TV (455 subscribers, 11K views)
https://www.youtube.com/channel/UChIaUcs3tho6XhyU6K6KMrw
博客專欄
下面我主要列了些那些持續發佈與人工智能相關主題的原創博客。
1、Andrej Karpathy
http://karpathy.github.io/
2、i am trask
http://iamtrask.github.io/
3、Christopher Olah
http://colah.github.io/
4、Top Bots
http://www.topbots.com/
5、WildML
http://www.wildml.com/
6、Distill
http://distill.pub/
7、Machine Learning Mastery
http://machinelearningmastery.com/blog/
8、FastML
http://fastml.com/
9、Adventures in NI
https://joanna-bryson.blogspot.de/
10、Sebastian Ruder
http://sebastianruder.com/
11、Unsupervised Methods
http://unsupervisedmethods.com/
12、Explosion
https://explosion.ai/blog/
13、Tim Dettmers
http://timdettmers.com/
14、When trees fall…
http://blog.wtf.sg/
15、ML@B
https://ml.berkeley.edu/blog/
Github
AI社區的好處之一是，大多數新項目都是開源的，可以在Github上使用。在Github上也有很多教育資源。
1、Machine Learning
https://github.com/search?o=desc&q=topic%3Amachine-learning+&s=stars&type=Repositories&utf8=%E2%9C%93
2、Deep Learning
https://github.com/search?q=topic%3Adeep-learning&type=Repositories
3、Tensorflow
https://github.com/search?q=topic%3Atensorflow&type=Repositories
4、Neural Network
https://github.com/search?q=topic%3Aneural-network&type=Repositories
5、NLP
https://github.com/search?utf8=%E2%9C%93&q=topic%3Anlp&type=Repositories
書籍推薦
市面上有很多關於機器學習、深度學習和NLP的書籍。在這一節中，我將只關注那些你可以直接從網上獲取或下載的免費書籍。
機器學習部分
1、Understanding Machine Learning From Theory to Algorithms
http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2、Machine Learning Yearning
http://www.mlyearning.org/
3、A Course in Machine Learning
http://ciml.info/
4、Machine Learning
https://www.intechopen.com/books/machine_learning
5、Neural Networks and Deep Learning
http://neuralnetworksanddeeplearning.com/
6、Deep Learning Book
http://www.deeplearningbook.org/
7、Reinforcement Learning: An Introduction
http://incompleteideas.net/sutton/book/the-book-2nd.html
8、Reinforcement Learning
https://www.intechopen.com/books/reinforcement_learning
NLP部分
1、Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
2、Natural Language Processing with Python
http://www.nltk.org/book/
3、An Introduction to Information Retrieval
https://nlp.stanford.edu/IR-book/html/htmledition/irbook.html
數學基礎部分
1、Introduction to Statistical Thought
http://people.math.umass.edu/~lavine/Book/book.pdf
2、Introduction to Bayesian Statistics
https://www.stat.auckland.ac.nz/~brewer/stats331.pdf
3、Introduction to Probability
https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf
4、Think Stats: Probability and Statistics for Python programmers
http://greenteapress.com/wp/think-stats-2e/
5、The Probability and Statistics Cookbook
http://statistics.zone/
6、Linear Algebra
http://joshua.smcvt.edu/linearalgebra/book.pdf
7、Linear Algebra Done Wrong
http://www.math.brown.edu/~treil/papers/LADW/book.pdf
8、Linear Algebra, Theory And Applications
https://math.byu.edu/~klkuttle/Linearalgebra.pdf
9、Mathematics for Computer Science
https://courses.csail.mit.edu/6.042/spring17/mcs.pdf
10、Calculus
https://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf
11、Calculus I for Computer Science and Statistics Students
http://www.math.lmu.de/~philip/publications/lectureNotes/calc1_forInfAndStatStudents.pdf
Quora
Quora已經成爲人工智能和機器學習的重要資源。許多頂尖的研究人員在網站上回答問題。下面我列出了一些主要的人工智能相關主題：
1、Computer-Science
https://www.quora.com/topic/Computer-Science
2、Machine-Learning
https://www.quora.com/topic/Machine-Learning
3、Artificial-Intelligence
https://www.quora.com/topic/Artificial-Intelligence
4、Deep-Learning
https://www.quora.com/topic/Deep-Learning
5、Natural-Language-Processing
https://www.quora.com/topic/Natural-Language-Processing
6、Classification-machine-learning
https://www.quora.com/topic/Classification-machine-learning
7、Artificial-General-Intelligence
https://www.quora.com/topic/Artificial-General-Intelligence
8、Convolutional-Neural-Networks-CNNs
https://www.quora.com/topic/Convolutional-Neural-Networks-CNNs
9、Computational-Linguistics
https://www.quora.com/topic/Computational-Linguistics
10、Recurrent-Neural-Networks
https://www.quora.com/topic/Recurrent-Neural-Networks
會議
不出所料，隨着人工智能的普及，與人工智能相關的會議數量也在增加。
學術
1、NIPS
https://nips.cc/
2、ICML
https://2017.icml.cc/
3、KDD
http://www.kdd.org/
4、ICLR
http://www.iclr.cc/
5、ACL
http://acl2017.org/
6、EMNLP
http://emnlp2017.net/
7、CVPR
http://cvpr2017.thecvf.com/
8、ICCF
http://iccv2017.thecvf.com/
專業
1、O’Reilly Artificial Intelligence Conference
https://conferences.oreilly.com/artificial-intelligence/
2、Machine Learning Conference
http://mlconf.com/
3、AI Expo
https://www.ai-expo.net/
4、AI Summit
https://theaisummit.com/
5、AI Conference
https://aiconference.ticketleap.com/helloworld/
關於更多機器學習、人工智能、增強現實資源和技術乾貨，可以關注公衆號：AIRX社區，共同學習，一起進步！
