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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

Mathematics | 数学基础

  • 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。书中的算法实现参考这里
  • 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.
  • 2022-Visualize ML #Book#: Book3_Fundamentals-of-Mathematics, Book4_Power-of-Matrix, Book5_Probability-and-Statistics, Book6_Data-Science, Book7_Machine-Learning。

Machine Learning | 机器学习

Data Mining

  • 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.

Reinforcement Learning | 强化学习

  • 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.
  • 2021-蘑菇书 EasyRL #Book#: 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 #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.
  • 2021-动手学习深度学习 #Project#: 《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

Computer Vision | 计算机视觉

DataScience | 泛数据科学

  • 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

Toolkits

TensorFlow

PyTorch

  • 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.
  • 2019-Dive into DL PyTorch #Book#: 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
  • https://www.zhihu.com/question/343407265/answer/830912894
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