2018 AI、机器学习、深度学习与 Tensorflow 相关优秀书籍、课程、示例链接集锦

摘要：机器学习深度学习与自然语言处理领域推荐的书籍列表人工智能深度学习与相关书籍课程示例列表是笔者系列的一部分对于其他的资料集锦模型开源工具与框架请参考。

人工智能、深度学习与 Tensorflow 相关书籍、课程、示例列表是笔者 Awesome Links 系列的一部分；对于其他的资料集锦、模型、开源工具与框架请参考 DataScienceAI Links Series。本文推荐的部分开源书籍可以前往 Awesome-CS-Books-Warehouse 便捷翻阅，或者前往 AI CheatSheet, AIDL-Series | 人工智能与深度学习实战, AIDL Workbench | 示例，算法，模型，应用了解更多细节内容与代码实现。

2008-统计学完全教程 #Book#：由美国当代著名统计学家 L·沃塞曼所著的《统计学元全教程》是一本几乎包含了统计学领域全部知识的优秀教材。本书除了介绍传统数理统计学的全部内容以外，还包含了 Bootstrap 方法(自助法)、独立性推断、因果推断、图模型、非参数回归、正交函数光滑法、分类、统计学理论及数据挖掘等统计学领域的新方法和技术。本书不但注重概率论与数理统计基本理论的阐述，同时还强调数据分析能力的培养。本书中含有大量的实例以帮助广大读者快速掌握使用 R 软件进行统计数据分析。

2009-Convex Optimization #Book#:This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved numerically very efficiently. The basic point of this book is that the same can be said for the larger class of convex optimization problems.

2009-The Elements of Statistical Learning: Data Mining, Inference, and Prediction-Second Edition: Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

2010-All of Statistics: A Concise Course in Statistical Inference #Book#: The goal of this book is to provide a broad background in probability and statistics for students in statistics, Computer science (especially data mining and machine learning), mathematics, and related disciplines.

2012-李航-统计方法学 #Book#: 本书全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k 近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与熵模型、支持向量机、提升方法、EM 算法、隐马尔可夫模型和条件随机场等。

2016-Immersive Linear Algebra #Book#: The World"s First Linear Algeria Book with fully Interactive Figures.

2017-The Mathematics of Machine Learning #Book#: Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

2007-Pattern Recognition And Machine Learning #Book#: The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

2012-Machine Learning A Probabilistic Perspective #Book#: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.

2014-The Cambridge Handbook of Artificial Intelligence #Book#: With a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI, but also to those in other disciplines wanting an authoritative and up-to-date introduction to the field.

2015-Data Mining, The Textbook #Book#: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.

2016-Dive into Machine Learning #Book#: I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself.

2016-周志华-机器学习 #Book#：机器学习》作为该领域的入门教材，在内容上尽可能涵盖机器学习基础知识的各方面。介绍了机器学习的基础知识，经典而常用的机器学习方法（决策树、神经网络、支持向量机、贝叶斯分类器、集成学习、聚类、降维与度量学习），特征选择与稀疏学习、计算学习理论、半监督学习、概率图模型、规则学习以及强化学习等。

2016-Prateek Joshi-Python Real World Machine Learning #Book#: Learn to solve challenging data science problems by building powerful machine learning models using Python.

2016-Designing Machine Learning Systems with Python: Gain an understanding of the machine learning design process, Optimize machine learning systems for improved accuracy, Understand common programming tools and techniques for machine learning, Develop techniques and strategies for dealing with large amounts of data from a variety of sources, Build models to solve unique tasks.

2018-AndrewNG-Machine Learning Yearning #Book#: This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer.

2018-Artificial Intelligence: A Modern Approach-3rd Edition #Book#:Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

2018-Reinforcement Learning: An Introduction-Second Edition #Book#: This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.

2015-Goodfellow, Bengio and Courville-The Deep Learning Textbook #Book#:中文译本这里，The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

2016-Stanford Deep Learning Tutorial #Book#: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

2016-Building Machine Learning Projects with TensorFlow #Book#: Engaging projects that will teach you how complex data can be exploited to gain the most insight.

2016-深度学习入门 #Book#：您现在在看的这本书是一本“交互式”电子书 —— 每一章都可以运行在一个 Jupyter Notebook 里。 我们把 Jupyter, PaddlePaddle, 以及各种被依赖的软件都打包进一个 Docker image 了。所以您不需要自己来安装各种软件，只需要安装 Docker 即可。

2017-Neural Networks and Deep Learning #Book#: Neural Networks and Deep Learning is a free online book. The book will teach you about: (1) Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. (2) Deep learning, a powerful set of techniques for learning in neural networks

2017-TensorFlow Book #Book#: Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

2016-Text Data Management and Analysis #Book#: A Practical Introduction to Information Retrieval and Text Mining

2017-DL4NLP-Deep Learning for NLP resources:
State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog.

2016-OpenCV: Computer Vision Projects with Python: Use OpenCV"s Python bindings to capture video, manipulate images, and track objects. Learn about the different functions of OpenCV and their actual implementations.

2012-深入浅出数据分析-中文版 #Book#: 深入浅出数据分析》以类似“章回小说”的活泼形式，生动地向读者展现优秀的数据分析人员应知应会的技术：数据分析基本步骤、实验方法、最优化方法、假设检验方法、贝叶斯统计方法、主观概率法、启发法、直方图法、回归法、误差处理、相关数据库、数据整理技巧；正文之后，意犹未尽地以三篇附录介绍数据分析十大要务、R 工具及 ToolPak 工具，在充分展现目标知识以外，为读者搭建了走向深入研究的桥梁。

2014-DataScience From Scratch #Book#: In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

2016-Python Data Science Handbook #Book#:Jupyter Notebooks for the Python Data Science Handbook

2010-MIT Artifical Intelligence Videos: This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

2014-斯坦福-机器学习课程 #Course#: 在本课程中，您将学习最高效的机器学习技术，了解如何使用这些技术，并自己动手实践这些技术。更重要的是，您将不仅将学习理论知识，还将学习如何实践，如何快速使用强大的技术来解决新问题。最后，您将了解在硅谷企业如何在机器学习和 AI 领域进行创新。

2014-Statistical Learning (Self-Paced) #Course#: This is an introductory-level course in supervised learning, with a focus on regression and classification methods.

2015-Udacity-Intro to Artificial Intelligence #Course#: In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

2016-台大机器学习技法 #Course#: Linear Support Vector Machine (SVM) :: Course Introduction @ Machine Learning Techniques, etc.

2017-EdX-Artificial Intelligence (AI) #Course#: Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

2018-Machine Learning Crash Course with TensorFlow APIs by Google #Course#: Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

2016-Deep Learning by Google #Course#: In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

2017-CS 20SI: TensorFlow for Deep Learning Research #Course#: This course will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.

2017-Fast.ai DeepLearning AI #Course#: Most of the library is quite well tested since many students have used it to complete the Practical Deep Learning for Coders course. However it hasn"t been widely used yet outside of the course, so you may find some missing features or rough edges.

2016-University of Illinois at Urbana-Champaign:Text Mining and Analytics #Course#: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

2017-Neural Networks for Machine Learning #Course#: Learn about artificial neural networks and how they"re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc.

2017-Oxford Deep NLP course #Course#: This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence.

2017-CS224d: Deep Learning for Natural Language Processing #Course#: Intro to NLP and Deep Learning, Simple Word Vector representations: word2vec, GloVe, etc.

2017-Artificial Intelligence for Robotics #Course#: Learn how to program all the major systems of a robotic car from the leader of Google and Stanford"s autonomous driving teams.

2015-Trained image classification models for Keras #Project#: Keras code and weights files for popular deep learning models.

All-in-one Docker image for Deep Learning #Project#: An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

Top Deep Learning Projects: A list of popular github projects related to deep learning (ranked by stars).

2017- TensorFlow demystified: To understand a new framework, Google’s TensorFlow is a framework for machine-learning calculations, it is often useful to see a ‘toy’ example and learn from it.

如何将 TensorFlow 用作计算框架: 如果你刚刚接触 TensorFlow 并想使用其来作为计算框架，那么本文是你的一个很好的选择，阅读它相信会对你有所帮助。

2017-We Need to Go Deeper: A Practical Guide to TensorFlow and Inception

2017-Top Five Use Cases of Tensorflow: TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation.

2018-Google Brain 研究员详解聊天机器人: 面临的深度学习技术问题以及基于 TensorFlow 的开发实践。

How Zendesk Serves TensorFlow Models in Production

2015-tensorflow_tutorials: From the basics to slightly more interesting applications of Tensorflow

2017-Effective TensorFlow: My attempt is to gradually expand this series by adding new articles and keep the content up to date with the latest releases of TensorFlow API.

2017-TensorFlow 101: TensorFlow is an open source machine learning library developed at Google. TensorFlow uses data flow graphs for numerical computations.

2017-TensorFlow-World: This repository is aimed to provide simple and ready-to-use tutorials for TensorFlow.

2015-TensorFlow Examples: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.

2016-Deep Learning Using Tensorflow: This repository contains the code for Tensorflow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

2017-Deep Learning 21 Examples: 本工程是《21 个项目玩转深度学习———基于 TensorFlow 的实践详解》的配套代码，代码推荐的运行环境为：Ubuntu 14.04，Python 2.7、TensorFlow >= 1.4.0。请尽量使用类 UNIX 系统和 Python 2 运行本书的代码。

2017-TensorFlow Models by Sarasra #Project#: This repository contains a number of different models implemented in TensorFlow: the official models, the research models, the samples folder and the tutorials folder.

Android TensorFlow Machine Learning Example: This article is for those who are already familiar with machine learning and know how to the build model for machine learning(for this example I will be using a pre-trained model).

2018-Deep Learning Using Tensorflow:
This repository contains the code for Tensorflow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.

2018-TensorFlow Project Template #Project#: A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here"s a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design.

2018-Beginner Tensorflowjs Examples in Javascript: This is the easiest set of Machine Learning examples that I can find or make. I hope you enjoy it.

Awesome TensorFlow #Collection#: A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.

TensorFlow-World-Resources #Collection#: Organized & Useful Resources about Deep Learning with TensorFlow

2016-TensorFlow in a Nutshell — Part One: Basics: The fast and easy guide to the most popular Deep Learning framework in the world.

2016-Tensorflow 架构: TF 的特点之一就是可以支持很多种设备，大到 GPU、CPU，小到手机平板，五花八门的设备都可以跑起来 TF。

2016-Image Completion with Deep Learning in TensorFlow

2017-NakedTensor: Bare bone examples of machine learning in TensorFlow.

2017-Deep Learning in 7 lines of code: The essence of machine learning is recognizing patterns within data. This boils down to 3 things: data, software and math. What can be done in seven lines of code you ask? A lot.

2017- TensorFlow 代码解析：本文由浅入深的阐述 Tensor 和 Flow 的概念。先介绍了 TensorFlow 的核心概念和基本概述，然后剖析了 OpKernels 模块、Graph 模块、Session 模块。

2017-TensorFlow 入门级解读：矩阵、多特征线性和逻辑回归：本文是日本东京 TensorFlow 聚会联合组织者 Hin Khor 所写的 TensorFlow 系列介绍文章。

2017-We Need to Go Deeper: A Practical Guide to TensorFlow and Inception