AI-Book-List

DataScienceAI Book List | 机器学习、深度学习与自然语言处理领域推荐的书籍列表

A curated list of Artificial Intelligence (AI) courses and books, aggerated with DataScienceAI-Book-List and DataScienceAI-Course-List from Awesome-Lists.

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 DataScience AI List & Series。

Machine Learning | 机器学习

2007-Pattern Recognition And Machine Learning》📚: 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》📚: 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》📚: 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》📚: 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》📚: 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》📚：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。
- 《机器学习》（西瓜书）公式推导解析
- 《机器学习》（西瓜书）代码实战
2016-Prateek Joshi-Python Real World Machine Learning》📚: 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-Andrew NG-Machine Learning Yearning》📚: 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. 中文版本参考这里。
2018-Artificial Intelligence: A Modern Approach-3rd Edition》📚: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》📚: This book is about making machine learning models and their decisions interpretable.
2019-Python Machine Learning》📚: The “Python Machine Learning (3nd edition)” book code repository.
2020-Algorithms for decision making》📚: This book provides a broad introduction to algorithms for decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them.
2020-Approaching (Almost) Any Machine Learning Problem》📚
2022-机器学习系统：设计和实现》📚: 本开源项目试图给读者讲解现代机器学习系统的设计原理和实现经验。
2022-Machine Learning Bookcamp》📚: Learn machine learning by doing projects and get the skills needed to work as a data scientist or machine learning engineer.

Data Mining

2019-Fundamentals of Analysis》📚: 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.

Reinforcement Learning | 强化学习

2018-Reinforcement Learning: An Introduction-Second Edition》📚: 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.
2021-蘑菇书 EasyRL》📚: 李宏毅老师的《深度强化学习》是强化学习领域经典的中文视频之一。李老师幽默风趣的上课风格让晦涩难懂的强化学习理论变得轻松易懂，他会通过很多有趣的例子来讲解强化学习理论。比如老师经常会用玩 Atari 游戏的例子来讲解强化学习算法。此外，为了教程的完整性，我们整理了周博磊老师的《强化学习纲要》、李科浇老师的《世界冠军带你从零实践强化学习》以及多个强化学习的经典资料作为补充。对于想入门强化学习又想看中文讲解的人来说绝对是非常推荐的。

DeepLearning | 深度学习

2015-Goodfellow, Bengio and Courville-The Deep Learning Textbook》📚: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》📚: 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》📚：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》📚: 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》📚: Here we have only included the code samples themselves and immediately related surrounding comments.
2018-深度学习 500 问》📚: 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》📚: 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》📚: 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 : 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)》📚: Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.
2021-动手学习深度学习 : 《动手学习深度学习》是李沐老师（AWS 资深首席科学家，美国卡内基梅隆大学计算机系博士）主讲的一系列深度学习视频。本项目收集了我们在寒假期间学习《动手学习深度学习》过程中详细的 markdown 笔记和相关的 jupyter 代码。赠人玫瑰，手留余香，我们将所有的 markdown 笔记开源，希望在自己学习的同时，也对大家学习掌握李沐老师的《动手学习深度学习》有所帮助。

NLP | 自然语言处理

2016-Text Data Management and Analysis》📚: 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》📚: 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》📚: 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》📚: 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。

Computer Vision | 计算机视觉

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.
2021-计算机视觉实战演练：算法与应用》📚: 作者系迈微 AI 研习社创始人、CSDN 博客专家，主要分享机器学习算法、计算机视觉等相关内容，每周研读顶会论文，持续关注前沿技术动态。公众号底部有菜单分类，关注我们，一起学习成长。

DataScience | 泛数据科学

2012-深入浅出数据分析-中文版》📚: 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》📚: 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》📚:Jupyter Notebooks for the Python Data Science Handbook
2019-Another Book on Data Science》📚: Learn R and Python in Parallel

Toolkits

TensorFlow

2016-Building Machine Learning Projects with TensorFlow》📚: Engaging projects that will teach you how complex data can be exploited to gain the most insight.
2017-TensorFlow 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》📚: 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》📚: Open source Deep Learning book, based on TensorFlow 2.0 framework.

PyTorch

2019-Deep Learning with PyTorch》📚: 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.
2019-Dive into DL PyTorch》📚: 本项目将《动手学深度学习》原书中 MXNet 代码实现改为 PyTorch 实现。原书作者：阿斯顿·张、李沐、扎卡里 C. 立顿、亚历山大 J. 斯莫拉以及其他社区贡献者，GitHub 地址：https://github.com/d2l-ai/d2l-zh