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.
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.
2017-G. Casella-An Introduction to Statistical Learning #Book#: This book is appropriate for advanced undergraduates or master’s students in statistics or related quantitative fields or for individuals in otherdisciplines who wish to use statistical learning tools to analyze their data.
2018-AI 算法工程师手册 #Book#: 这是作者多年以来学习总结的笔记，经整理之后开源于世。目前还有约一半的内容在陆续整理中，已经整理好的内容放置在此。
2019-Mathematics for Machine Learning #Book#: We wrote a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.
2019-The Little Handbook of Statistical Practice #Book#: This is about statistical practice--what happens when a statistician (me) deals with data on a daily basis.
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-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-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.
2019-Interpretable Machine Learning #Book#: This book is about making machine learning models and their decisions interpretable.
2019-Python Machine Learning #Book#: The "Python Machine Learning (3nd edition)" book code repository.
2019-Fundamentals of Analysis #Book#: You have data, now how do you analyze it correctly? This is not a simple task, this book will cover common techniques to get insights out of data accurately.
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-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-Deep Learning with Python #Book#: Here we have only included the code samples themselves and immediately related surrounding comments.
2018-深度学习 500 问 #Book#: 以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。
2019-深度学习理论与实战：提高篇 #Book#: 本书的目标是使用通俗易懂的语言来介绍基础理论和最新的进展，同时也介绍代码的实现。通过理论与实践的结合使读者更加深入的理解理论知识，同时也把理论知识用于指导实践。
2019-动手学深度学习 #Book#: 这是一本深度学习在线书，其使用 Apache MXNet 的最新 gluon 接口来演示如何从 0 开始实现深度学习的各个算法。作者利用 Jupyter notebook 能将文档、代码、公式和图形统一在一起的优势，提供了一个交互式的学习体验。
2019-神经网络与深度学习 #Project#: 本课程主要介绍神经网络与深度学习中的基础知识、主要模型（前馈网络、卷积网络、循环网络等）以及在计算机视觉、自然语言处理等领域的应用。
2020-Dive into Deep Learning (D2L.ai) #Book#: Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.
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.
2017-Li Deng-Deep Learning in Natural Language Processing #Book#: this book provides comprehensive introduction to and up-to-date review of the state of art in applying deep learning to solve fundamental problems in NLP.
2018-Dan Jurafsky-Speech and Language Processing-3rd #Book#: New pedagogical sequences on neural networks and their training, starting with logistic regression and continuing with embeddings, feed-forward nets, and RNNs.
2020-机器翻译：统计建模与深度学习方法 #Book#: 这是一个教程，目的是对机器翻译的统计建模和深度学习方法进行较为系统的介绍。其内容被编纂成书，可以供计算机相关专业高年级本科生及研究生学习之用，亦可作为自然语言处理，特别是机器翻译相关研究人员的参考资料。本书用 tex 编写，所有源代码均已开放。
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
2019-Another Book on Data Science #Book#: Learn R and Python in Parallel
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.
2017-TensorFlow Book #Book#: Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
2019-简单粗暴 TensorFlow 2.0 | A Concise Handbook of TensorFlow 2.0 #Book#: 这是一本简明的 TensorFlow 2.0 入门指导手册，基于 Keras 和 Eager Execution（即时运行）模式，力图让具备一定机器学习及 Python 基础的开发者们快速上手 TensorFlow 2.0。
2019-深度学习开源书，基于 TensorFlow 2.0 实战 #Book#: Open source Deep Learning book, based on TensorFlow 2.0 framework.
2019-Deep Learning with PyTorch #Book#: This book is intended to be a starting point for software engineers, data scientists, and motivated students who are fluent in Python and want to become comfortable using PyTorch to build deep learning projects.