摘要：機(jī)器學(xué)習(xí)深度學(xué)習(xí)與自然語言處理領(lǐng)域推薦的書籍列表人工智能深度學(xué)習(xí)與相關(guān)書籍課程示例列表是筆者系列的一部分對(duì)于其他的資料集錦模型開源工具與框架請(qǐng)參考。

人工智能、深度學(xué)習(xí)與 Tensorflow 相關(guān)書籍、課程、示例列表是筆者 Awesome Links 系列的一部分；對(duì)于其他的資料集錦、模型、開源工具與框架請(qǐng)參考 DataScienceAI Links Series。本文推薦的部分開源書籍可以前往 Awesome-CS-Books-Warehouse 便捷翻閱，或者前往 AI CheatSheet, AIDL-Series | 人工智能與深度學(xué)習(xí)實(shí)戰(zhàn), AIDL Workbench | 示例，算法，模型，應(yīng)用了解更多細(xì)節(jié)內(nèi)容與代碼實(shí)現(xiàn)。

2008-統(tǒng)計(jì)學(xué)完全教程 #Book#：由美國當(dāng)代著名統(tǒng)計(jì)學(xué)家 L·沃塞曼所著的《統(tǒng)計(jì)學(xué)元全教程》是一本幾乎包含了統(tǒng)計(jì)學(xué)領(lǐng)域全部知識(shí)的優(yōu)秀教材。本書除了介紹傳統(tǒng)數(shù)理統(tǒng)計(jì)學(xué)的全部內(nèi)容以外，還包含了 Bootstrap 方法(自助法)、獨(dú)立性推斷、因果推斷、圖模型、非參數(shù)回歸、正交函數(shù)光滑法、分類、統(tǒng)計(jì)學(xué)理論及數(shù)據(jù)挖掘等統(tǒng)計(jì)學(xué)領(lǐng)域的新方法和技術(shù)。本書不但注重概率論與數(shù)理統(tǒng)計(jì)基本理論的闡述，同時(shí)還強(qiáng)調(diào)數(shù)據(jù)分析能力的培養(yǎng)。本書中含有大量的實(shí)例以幫助廣大讀者快速掌握使用 R 軟件進(jìn)行統(tǒng)計(jì)數(shù)據(jù)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-統(tǒng)計(jì)方法學(xué) #Book#: 本書全面系統(tǒng)地介紹了統(tǒng)計(jì)學(xué)習(xí)的主要方法，特別是監(jiān)督學(xué)習(xí)方法，包括感知機(jī)、k 近鄰法、樸素貝葉斯法、決策樹、邏輯斯諦回歸與熵模型、支持向量機(jī)、提升方法、EM 算法、隱馬爾可夫模型和條件隨機(jī)場等。

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-周志華-機(jī)器學(xué)習(xí) #Book#：機(jī)器學(xué)習(xí)》作為該領(lǐng)域的入門教材，在內(nèi)容上盡可能涵蓋機(jī)器學(xué)習(xí)基礎(chǔ)知識(shí)的各方面。介紹了機(jī)器學(xué)習(xí)的基礎(chǔ)知識(shí)，經(jīng)典而常用的機(jī)器學(xué)習(xí)方法（決策樹、神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)、貝葉斯分類器、集成學(xué)習(xí)、聚類、降維與度量學(xué)習(xí)），特征選擇與稀疏學(xué)習(xí)、計(jì)算學(xué)習(xí)理論、半監(jiān)督學(xué)習(xí)、概率圖模型、規(guī)則學(xué)習(xí)以及強(qiáng)化學(xué)習(xí)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#:中文譯本這里，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-深度學(xué)習(xí)入門 #Book#：您現(xiàn)在在看的這本書是一本“交互式”電子書 —— 每一章都可以運(yùn)行在一個(gè) Jupyter Notebook 里。 我們把 Jupyter, PaddlePaddle, 以及各種被依賴的軟件都打包進(jìn)一個(gè) Docker image 了。所以您不需要自己來安裝各種軟件，只需要安裝 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-深入淺出數(shù)據(jù)分析-中文版 #Book#: 深入淺出數(shù)據(jù)分析》以類似“章回小說”的活潑形式，生動(dòng)地向讀者展現(xiàn)優(yōu)秀的數(shù)據(jù)分析人員應(yīng)知應(yīng)會(huì)的技術(shù)：數(shù)據(jù)分析基本步驟、實(shí)驗(yàn)方法、最優(yōu)化方法、假設(shè)檢驗(yàn)方法、貝葉斯統(tǒng)計(jì)方法、主觀概率法、啟發(fā)法、直方圖法、回歸法、誤差處理、相關(guān)數(shù)據(jù)庫、數(shù)據(jù)整理技巧；正文之后，意猶未盡地以三篇附錄介紹數(shù)據(jù)分析十大要?jiǎng)?wù)、R 工具及 ToolPak 工具，在充分展現(xiàn)目標(biāo)知識(shí)以外，為讀者搭建了走向深入研究的橋梁。

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-機(jī)器學(xué)習(xí)課程 #Course#: 在本課程中，您將學(xué)習(xí)最高效的機(jī)器學(xué)習(xí)技術(shù)，了解如何使用這些技術(shù)，并自己動(dòng)手實(shí)踐這些技術(shù)。更重要的是，您將不僅將學(xué)習(xí)理論知識(shí)，還將學(xué)習(xí)如何實(shí)踐，如何快速使用強(qiáng)大的技術(shù)來解決新問題。最后，您將了解在硅谷企業(yè)如何在機(jī)器學(xué)習(xí)和 AI 領(lǐng)域進(jìn)行創(chuàng)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-臺(tái)大機(jī)器學(xué)習(xí)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.

如何將 TensorFlow 用作計(jì)算框架: 如果你剛剛接觸 TensorFlow 并想使用其來作為計(jì)算框架，那么本文是你的一個(gè)很好的選擇，閱讀它相信會(huì)對(duì)你有所幫助。

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 研究員詳解聊天機(jī)器人: 面臨的深度學(xué)習(xí)技術(shù)問題以及基于 TensorFlow 的開發(fā)實(shí)踐。

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 個(gè)項(xiàng)目玩轉(zhuǎn)深度學(xué)習(xí)———基于 TensorFlow 的實(shí)踐詳解》的配套代碼，代碼推薦的運(yùn)行環(huán)境為：Ubuntu 14.04，Python 2.7、TensorFlow >= 1.4.0。請(qǐng)盡量使用類 UNIX 系統(tǒng)和 Python 2 運(yùn)行本書的代碼。

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 架構(gòu): TF 的特點(diǎn)之一就是可以支持很多種設(shè)備，大到 GPU、CPU，小到手機(jī)平板，五花八門的設(shè)備都可以跑起來 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 代碼解析：本文由淺入深的闡述 Tensor 和 Flow 的概念。先介紹了 TensorFlow 的核心概念和基本概述，然后剖析了 OpKernels 模塊、Graph 模塊、Session 模塊。

2017-TensorFlow 入門級(jí)解讀：矩陣、多特征線性和邏輯回歸：本文是日本東京 TensorFlow 聚會(huì)聯(lián)合組織者 Hin Khor 所寫的 TensorFlow 系列介紹文章。

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