Awesome Links of Books, Courses, Examples for AI, MachineLearning, DeepLearning and Tensorflow in 2018

DataScienceAI Book Links | 機器學習、深度學習與自然語言處理領域推薦的書籍列表

Mathematics | 數學基礎

• 2008-統計學完全教程 #Book#：由美國當代著名統計學家 L·沃塞曼所著的《統計學元全教程》是一本幾乎包含了統計學領域全部知識的優秀教材。本書除了介紹傳統數理統計學的全部內容以外，還包含了 Bootstrap 方法(自助法)、獨立性推斷、因果推斷、圖模型、非引數迴歸、正交函式光滑法、分類、統計學理論及資料探勘等統計學領域的新方法和技術。本書不但注重概率論與數理統計基本理論的闡述，同時還強調資料分析能力的培養。本書中含有大量的例項以幫助廣大讀者快速掌握使用 R 軟體進行統計資料分析。
• 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-統計方法學 #Book#: 本書全面系統地介紹了統計學習的主要方法，特別是監督學習方法，包括感知機、k 近鄰法、樸素貝葉斯法、決策樹、邏輯斯諦迴歸與熵模型、支援向量機、提升方法、EM 演算法、隱馬爾可夫模型和條件隨機場等。
• 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.

Machine Learning | 機器學習

• 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-機器學習 #Book#：機器學習》作為該領域的入門教材，在內容上儘可能涵蓋機器學習基礎知識的各方面。介紹了機器學習的基礎知識，經典而常用的機器學習方法（決策樹、神經網路、支援向量機、貝葉斯分類器、整合學習、聚類、降維與度量學習），特徵選擇與稀疏學習、計算學習理論、半監督學習、概率圖模型、規則學習以及強化學習等。
• 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.

Reinforcement Learning | 強化學習

• 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.

DeepLearning | 深度學習

• 2015-Goodfellow, Bengio and Courville-The Deep Learning Textbook #Book#:中文譯本這裡，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-深度學習入門 #Book#：您現在在看的這本書是一本“互動式”電子書 — — 每一章都可以執行在一個 Jupyter Notebook 裡。 我們把 Jupyter, PaddlePaddle, 以及各種被依賴的軟體都打包進一個 Docker image 了。所以您不需要自己來安裝各種軟體，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.

NLP | 自然語言處理

• 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.

Computer Vision | 計算機視覺

• 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.

DataScience | 泛資料科學

• 2012-深入淺出資料分析-中文版 #Book#: 深入淺出資料分析》以類似“章回小說”的活潑形式，生動地向讀者展現優秀的資料分析人員應知應會的技術：資料分析基本步驟、實驗方法、最優化方法、假設檢驗方法、貝葉斯統計方法、主觀概率法、啟發法、直方圖法、迴歸法、誤差處理、相關資料庫、資料整理技巧；正文之後，意猶未盡地以三篇附錄介紹資料分析十大要務、R 工具及 ToolPak 工具，在充分展現目標知識以外，為讀者搭建了走向深入研究的橋樑。
• 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.