自从阿法狗赢了李世石，人工智能这个概念就从小众迅速走向大众，之后又战胜柯洁，各种“未来已来”的声音此起彼伏，火遍整个星球。
有很多未入门，想入门人工智能的童鞋很迷茫，不知道如何选学习资料，甚至还有的在抱怨学习资料太少。
其实，学习资料不是太少，而是太多，但是正好合适自己的太少。
首先要提的是3本书，中文读者的必读书目，按出版时间分别是
1.《统计机器方法》 作者是人工智能领军科学家 工业界大神 李航老师
2.《机器学习》（昵称 “瓜书”，也称西瓜书），作者是人工智能领军科学家 学术界大神 周志华教授
3.《深度学习》（昵称 “花书”，被誉为深度学习领域的圣经），英文版 原作者是 深度学习三始祖之一的 Yoshua Bengio 及其他两位超级大神，中译者是人工智能领军科学家 学术界大神 张志华老师及其得意弟子赵申剑 等人
对于选择学习资料，楼主有个观点，最优先的原则是“有问题能找到回答的人”，至少符合以下3个条件中的1个：
- 能联系到资料的作者
- 身边有朋友正在看（不久前看过）这些资料
- 经典书籍或权威资料，这样可以保障有最多的人群覆盖
如果各位童鞋同意上述观点，那么，好消息来啦，黄海广博士率领一众小伙伴整理了吴恩达教授的深度学习课（deeplearning.ai）
https://github.com/fengdu78/deeplearning_ai_books
deeplearning.ai 是当下最权威的入门课程之一，楼主的身边已经有多位小伙伴完成了通关任务。强烈建议童鞋们以这门课作为起点，进入人工智能这个前景无限的领域。
没梯子的优先看这里
https://mooc.study.163.com/course/2001281002#/info
有梯子的或者人肉翻出去的当然看这里
https://www.coursera.org/specializations/deep-learning
基础
基础分为两大类别，一类是深度学习，另一类是传统的机器学习。
最权威的零基础入门深度学习-deeplearningbook
英语好的童鞋直接从头开始看这个吧
http://www.deeplearningbook.org/
原版作者：
Yoshua Bengio（DL 三巨头之一）, Ian Goodfellow（GAN 作者）, Aaron Courville, MIT Press
PDF 版
https://github.com/HFTrader/DeepLearningBook
中译版
https://github.com/exacity/deeplearningbook-chinese
但是译者自己也建议大家阅读原版哦
从数学基础开始，到 CNN，主要目录如下：
Part I: Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
Part II: Modern Practical Deep Networks
6 Deep Feedforward Networks
7 Regularization for Deep Learning
8 Optimization for Training Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications
Part III: Deep Learning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models
微信官方支持的
http://course.fast.ai/start.html
韩老师的零基础入门深度学习系列
机器学习书单
李航老师的《统计学习方法》，也是忆臻兄弟的主要参考
以下两本书，是阿里的同学们正在看的
https://book.douban.com/subject/2061116/
https://book.douban.com/subject/10758624/
忆臻兄弟的传统机器学习系列
浅析感知机（三）– 收敛性证明与对偶形式以及python代码讲解
tensorflow 官方文档
[Getting Started With TensorFlow](Getting Started With TensorFlow)
IBM 的系列视频
IBM中国研究院认知计算系列课程从科研和产业相结合的角度，深入浅出地介绍了认知计算和人工智能技术的起源、发展和未来方向，以及机器学习和深度学习的基本、工具和应用。
坦白说，从教学设计的角度，略显不足。
进阶
一大波高质量论文，涵盖以下经典副本
MNIST
CIFAR-10
CIFAR-100
STL-10
SVHN
ILSVRC2012 task 1
高阶
以下推荐据说是祖神 Michael Jordan（为啥叫祖神？因为是多位大神 Bengio、吴恩达的老师）为想入他门下的骚年列出的书单
1.) Casella, G. and Berger, R.L. (2001). "Statistical Inference" Duxbury Press.
For a slightly more advanced book that's quite clear on mathematical techniques, the following book is quite good:
2.) Ferguson, T. (1996). "A Course in Large Sample Theory" Chapman & Hall/CRC.
You'll need to learn something about asymptotics at some point, and a good starting place is:
3.) Lehmann, E. (2004). "Elements of Large-Sample Theory" Springer.
Those are all frequentist books. You should also read something Bayesian:
4.) Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC.
and you should start to read about Bayesian computation:
5.) Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer.
On the probability front, a good intermediate text is:
6.) Grimmett, G. and Stirzaker, D. (2001). "Probability and Random Processes" Oxford.
At a more advanced level, a very good text is the following:
7.) Pollard, D. (2001). "A User's Guide to Measure Theoretic Probability" Cambridge.
The standard advanced textbook is Durrett, R. (2005). "Probability: Theory and Examples" Duxbury.
Machine learning research also reposes on optimization theory. A good starting book on linear optimization that will prepare you for convex optimization:
8.) Bertsimas, D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization" Athena.
And then you can graduate to:
9.) Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization" Cambridge.
Getting a full understanding of algorithmic linear algebra is also important. At some point you should feel familiar with most of the material in
10.) Golub, G., and Van Loan, C. (1996). "Matrix Computations" Johns Hopkins.
It's good to know some information theory. The classic is:
11.) Cover, T. and Thomas, J. "Elements of Information Theory" Wiley.
Finally, if you want to start to learn some more abstract math, you might want to start to learn some functional analysis (if you haven't already). Functional analysis is essentially linear algebra in infinite dimensions, and it's necessary for kernel methods, for nonparametric Bayesian methods, and for various other topics. Here's a book that I find very readable:
12.) Kreyszig, E. (1989). "Introductory Functional Analysis with Applications" Wiley.
GitHub 链接
https://github.com/MachineIntellect/DeepLearningEntryPoint/blob/master/DeepLearningEntryPoint.md
