精选的AI和机器学习资源清单

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本部分资源内容主要是国外的一些AI学习与开发内容，包括AI组织，视频课程，博客，书籍，YouTube频道，Quora，Github，书籍推荐，会议，研究链接，教程等。

组织机构

有一些著名的组织致力于推动AI研究与开发。

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、微软研究院

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/

视频课程

现在网上有大量的视频课程和教程，其中很多都是免费的，也有一些不错的付费选择，但在本文中，我只列举一些免费内容。

1、Coursera-机器学习

https://www.coursera.org/learn/machine-learning#syllabus

2、Coursera —机器学习的神经网络

https://www.coursera.org/learn/neural-networks

3、Udacity —机器学习入门

https://classroom.udacity.com/courses/ud120

4、Udacity —机器学习

https://www.udacity.com/course/machine-learning--ud262

5、Udacity —深度学习

https://www.udacity.com/course/deep-learning--ud730

6、机器学习

https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

7、面向程序员的实用深度学习

http://course.fast.ai/start.html

8、Stanford—用于视觉识别的卷积神经网络

https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA

9、Stanford—具有深度学习的自然语言处理

https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6

10、牛津大学深层自然语言处理课程

https://github.com/oxford-cs-deepnlp-2017/lectures

11、Python实用机器学习教程

https://www.youtube.com/watch?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&v=OGxgnH8y2NM

Youtube精选

下面提供了一些YouTube频道或用户的链接，这些频道或用户具有与AI或机器学习相关的常规内容。

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

博客专栏

下面我主要列了些那些持续发布与人工智能相关主题的原创博客。

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社区的好处之一是，大多数新项目都是开源的，可以在Github上使用。在Github上也有很多教育资源。

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

书籍推荐

市面上有很多关于机器学习、深度学习和NLP的书籍。在这一节中，我将只关注那些你可以直接从网上获取或下载的免费书籍。

机器学习部分

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

数学基础部分

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在网站上回答问题。下面我列出了一些主要的人工智能相关主题：

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

会议

不出所料，随着人工智能的普及，与人工智能相关的会议数量也在增加。

学术

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/

专业

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/