一、免费书籍随意读：
1．《深度学习之美：AI时代的数据处理与最佳实践》
本书作者张玉宏博士在阿里云云栖社区为读者免费呈现部分书中文字：
人工“碳”索意犹尽，智能“硅”来未可知（深度学习入门系列之二）
“机器学习”三重门，“中庸之道”趋若人（深度学习入门系列之四）
Hello World感知机，懂你我心才安息(深度学习入门系列之五)
云栖专题地址：https://yq.aliyun.com/topic/111。如果阅读后你对本书有些好感，可以移驾前往各大电商平台购买，本书正在火爆预售中！有兴趣可以点击，天猫链接购买！记得来云栖社区可以体验“先尝后买”！
2．实用好书免费读：数据科学与命令行《Data Science at the Command Line》by Jeroen Janssens
书中代码地址：https://github.com/jeroenjanssens/data-science-at-the-command-line
3．面向数据科学的Python数值计算/机器学习算法之旅：《Python's tours - A Numerical Tour of Data Science》
4．Python机器学习笔记/教程《Practice and tutorial-style notebooks covering wide variety of machine learning techniques》by Tirthajyoti Sarkar GitHub地址:
https://github.com/tirthajyoti/PythonMachineLearning
5．免费电子书：AI文集《Artificial Inteligence | GitBook》by Leonardo Araujo dos Santos
6．用python进行深度学习《deep-learning-with-python》
7．新书草稿：机器学习数学基础《Mathematics for Machine Learning》by Marc Peter Deisenroth，A Aldo Faisal, and Cheng Soon Ong (Cambridge 2018) GitHub地址:https://mml-book.github.io/
8．(R语言)统计与数据科学导论《An Introduction to Statistical and Data Sciences via R》by Chester Ismay and Albert Y. Kim (2017)
9．免费书：用实用方法讲授Python基础知识《Getting up to speed with Python: a book for self-learners》 by João Ventura GitHub地址:https://github.com/joaoventura/full-speed-python
10．《scikit-learn(sklearn) 官方文档中文版》
11．新书草稿：机器学习数学基础《Mathematics for Machine Learning》by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong (Cambridge 2018)
12．新书：Python机器学习方案手册《Machine Learning with Python Cookbook》by Chris Albon (O'Reilly 2018)
https://www.safaribooksonline.com/library/view/machine-learning-with/9781491989371/
http://shop.oreilly.com/product/0636920085423.do
13．免费书：R语言机器学习导论《An Introduction to Machine Learning with R》by Laurent Gatto (2017)
14．书稿：特征工程与特征选择——实用预测模型方法《Feature Engineering and Selection: A Practical Approach for Predictive Models》by Max Kuhn, Kjell Johnson
16. 由社区协同编写的算法书《The Arcane Algorithm Archive》
二．学习代码看这里
1．深度学习框架PyTorch：入门与实践书中代码：by Yun Chen.GitHub地址:https://github.com/chenyuntc/PyTorch-book
2．《R语言深度学习》随书代码《R Markdown Notebooks for "Deep Learning with R"》 GitHub地址:
https://github.com/jjallaire/deep-learning-with-r-notebooks
3．21个项目玩转深度学习——基于TensorFlow的实践详解配套代码 by Zhi yuan He GitHub地址:
https://github.com/hzy46/Deep-Learning-21-Examples
4．数据科学家Python手册：Python基础/数据结构/流程与函数/NumPy/Pandas《Python for Data Scientists》by Stephen Rimac GitHub:
5．160+道算法与数据结构练习题(C++)《160+ Algorithm & Data Structure Problems using C++》by Ravi Mandliya GitHub地址:
https://github.com/mandliya/algorithms_and_data_structures
三、数据集集合随意用
1．深度学习探险：最新深度学习文献、框架和资源集合：《Adventures in deep learning-State-of-the-art Deep Learning publications, frameworks & resources》by Grigorios Kalliatakis GitHub地址:
https://github.com/GKalliatakis/Adventures-in-deep-learning
2．中文文本项目：一个在线开放式数字图书馆，为全世界的读者和研究人员提供前现代化的中文文本。“中国文字计划”拥有三万多个标题，五十多亿字，是现存中国文字的最大数据库《Chinese Text Project》
3．医疗领域机器学习数据集列表《Medical Data for Machine Learning》by Andrew L. Beam GitHub地址:
https://github.com/beamandrew/medical-data
4．(又一个)自然语言处理(NLP)数据集列表《nlp-datasets - Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)》by Nicolas Iderhoff GitHub地址:
https://github.com/niderhoff/nlp-datasets
5. 自然语言SQL生成数据集(自然语言&SQL查询问题对)《text2sql-data - A collection of datasets that pair questions with SQL queries.》 by Jonathan Kummerfeld GitHub地址:
https://github.com/jkkummerfeld/text2sql-data
6．股价与推特数据集《stocknet-dataset - A comprehensive dataset for stock movement prediction from tweets and historical stock prices.》by Yumo Xu GitHub地址:
https://github.com/yumoxu/stocknet-dataset
四、其他学习材料
1．神经网络和深度学习学习材料：
http://neuralnetworksanddeeplearning.com/
https://github.com/mnielsen/neural-networks-and-deep-learning
2．NumPy学习资源精选《NumPy and Linear Algebra | Navigating ML》
3．Pandas速查：DataFrame基础《Python Pandas: DataFrame Basics》by Prince Grover
4．‘CS224n深度学习自然语言处理’课程设计报告集锦(2018)《CS224n: Natural Language Processing with Deep Learning》
