Python Artificial Intelligence Development from Beginner to Master

Name: Python Artificial Intelligence Development from Beginner to Master
ISBN: 9787301313039

杨柳 / 郭坦 / 鲁银芝Peking University Press · ChinaAug 2025

3.6

Word count: 215K words
Chapters: 106
ISBN: 9787301313039

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Summary

This book mainly introduces the technologies, infrastructure, core concepts, implementation methods and processes, and practical applications needed for AI development with Python.

The book is divided into 3 parts. Part 1 mainly explains the introductory knowledge of the Python programming language commonly used in AI development, including: Chapter 1 About Python and Development Environment Configuration, Chapter 2 Python Programming Basics, Chapter 3 Advanced Python Programming; Part 2 mainly explains the application of AI development-related knowledge, including: Chapter 4 Introduction to Artificial Intelligence, Chapter 5 Theoretical Foundations of Machine Learning, Chapter 6 Application of Common Python Machine Learning Libraries, Chapter 7 The First Machine Learning Project, Chapter 8 Typical Machine Learning Algorithms and Practical Applications, Chapter 9 Deep Learning Algorithm Theory, Chapter 10 TensorFlow for Deep Learning; Part 3, through 3 comprehensive cases, uses the important application of neural networks in computer vision as a thread to introduce the practice of deep learning AI technology in computer vision tasks, including: Chapter 11 AI Recognizes All Things, Part 3: Chapter 12 AI Knows All Things, Chapter 13 AI Draws All Things.

Editor's review

This book uses Python as its tool and advances through three parts—Getting Started, Theory and Methods, and Comprehensive Practice—to teach AI development. Part One covers environment setup and Python fundamentals and advanced topics, helping readers build a programming foundation; Part Two moves from an introduction to AI, machine learning theory, and common library applications to a first machine learning project and hands-on practice with typical algorithms, then into deep learning theory and TensorFlow, forming a continuous path from traditional machine learning to deep learning; Part Three takes computer vision as its thread and, through three comprehensive cases—recognizing all things, knowing all things, and drawing all things—grounds neural network applications in concrete tasks. The overall structure is clear, with theory and practice alternating, making it suitable as a route-based textbook for systematically learning AI development. Its highlight lies in linking knowledge points through project-driven learning; common libraries and TensorFlow are both covered, and the comprehensive cases help build awareness of a complete development workflow. Readers should not merely read the theory straight through; they should run the code, adjust parameters, and record results as they go. It suits readers with initial programming willingness who hope to build AI development skills from scratch and advance part by part.

Who it's for

A good fit for

Learners with a small amount of programming background who want a systematic introduction to AI development
College students who need to complete machine learning and deep learning practice projects alongside coursework
Working technical professionals who want to use Python as a tool to move into AI application development
Readers interested in computer vision cases who need training in a complete project workflow

Not a good fit for

Readers with no programming experience at all who are unwilling to build up Python fundamentals; the introductory part still moves at a fairly quick pace
Popular-science readers who only want to understand AI concepts and do not intend to write code
Senior practitioners with extensive deep learning engineering experience who are seeking breakthroughs in frontier research

Reading tips

Advance in the order of the three parts: first make sure the Python fundamentals and common library environment run properly, then move on to machine learning projects.
Run the code for each chapter by hand at least once, change parameters to observe how results vary, and record errors and how they were solved.
When studying typical algorithms, first understand the task and evaluation metrics, then compare them with the library function implementation; avoid merely memorizing how to call functions.
In the comprehensive case stage, first try to break down the workflow independently, then compare it with the book's implementation, and form your own project notes.

About the publisher

Peking University Press China · Founded in 1979

Peking University Press is a comprehensive university press whose publishing areas cover classical literature, popular science, social culture, world history, Western philosophy, and culture. Its catalogue contains 6,031 titles, with representative books including A Dream of Red Mansions, Relativity: The Special and General Theory (15th Edition) (with Einstein's classic papers on unified field theory and other topics), Elements, Conics, The Aristocratic Families of the Eastern Jin, The Philosophy of Mythology (Volume I), The Philosophy of Mythology (Volume II), and The Origin of Continents and Oceans. Representative authors include Cao Xueqin, Zhou Ruchang, Albert Einstein, Euclid, Apollonius, Tian Yuqing, Schelling, and Xian Gang. The press has developed distinctive strengths in classical Chinese literature and in the translation of Western philosophical classics, while also covering scientific classics and social and cultural studies. As public information is limited, this description is based only on the books in the catalogue and offers no further extrapolation.

版权信息
前言人工智能时代，创造无限价值
第1篇 Python基础篇
第1章关于Python与开发环境配置
1.1 Python入门
1.2 Python开发环境的配置
新手问答
本章小结
第2章 Python编程基础
2.1 基础语法
2.2 数据类型
2.3 逻辑控制语句
2.4 函数
新手问答
小试牛刀
本章小结
第3章 Python编程进阶
3.1 高级变量
3.2 面向对象编程
3.3 Python模块
3.4 python神经网络小实例
新手问答
小试牛刀
本章小结
第2篇人工智能篇
第4章人工智能简介
4.1 人工智能概述
4.2 人工智能崛起的三大基石
4.3 深度学习的重要性
新手问答
本章小结
第5章机器学习理论基础
5.1 机器学习概述
5.2 机器学习的4个分支
5.3 评估模型指标
5.4 数据预处理、特征工程和特征学习
5.5 过拟合与欠拟合
5.6 机器学习通用工作流程
新手问答
小试牛刀
本章小结
第6章 Python机器学习常用库的应用
6.1 NumPy—基础科学计算库
6.2 Pandas—数据分析的利器
6.3 Matplotlib—画出优美的图形
6.4 scikit-learn—非常流行的Python机器学习库
新手问答
小试牛刀
本章小结
第7章第一个机器学习项目
7.1 入门项目简介
7.2 数据导入
7.3 数据探索
7.4 数据可视化
7.5 算法评估
7.6 预测实施
新手问答
小试牛刀
本章小结
第8章典型的机器学习算法及应用实战
8.1 k-近邻算法
8.2 朴素贝叶斯分类算法
8.3 支持向量机
8.4 PCA算法
8.5 k-均值算法
新手问答
小试牛刀
本章小结
第9章深度学习算法理论
9.1 深度学习基础
9.2 神经网络
9.3 卷积神经网络
9.4 循环神经网络
新手问答
小试牛刀
本章小结
第10章深度学习之TensorFlow
10.16 主流的深度学习框架
10.26 TensorFlow环境搭建
10.36 TensorFlow基本知识
10.46 TensorFlow编程准备
10.56 TensorFlow基本开发步骤
10.66 TensorFlow的可视化
新手问答
小试牛刀
本章小结
第3篇实战案例篇
第11章人工智能识万物
11.1 卷积神经网络的前世今生
11.26 如何构建更深的神经网络
11.36 神经网络的可迁移性
新手问答
本章小结
第12章人工智能知万物
12.16 区域卷积神经网络
12.26 快速区域卷积神经网络6
12.36 更快区域卷积神经网络
12.46 YOLO网络
新手问答
本章小结
第13章人工智能绘万物
13.16 神经艺术风格迁移
13.26 基于TensorFlow的图像风格化实现
新手问答
本章小结
参考文献

FAQ

Can I read this book without any programming background?

You can try, but you need to invest time in building the fundamentals. Part One starts with Python and development environment setup and covers programming fundamentals and advanced topics, leaving an entry point for absolute beginners. However, the later machine learning and deep learning content requires some ability to read and debug code. It is advisable to write small programs while reading the introductory part, and to enter Part Two only after you can independently run and modify the sample code; otherwise you may struggle with library calls and error handling.

What machine learning and deep learning content does the book cover?

According to the introduction, Part Two includes an introduction to AI, the theoretical foundations of machine learning, applications of common Python machine learning libraries, a first machine learning project, typical machine learning algorithms and hands-on applications, as well as deep learning algorithm theory and TensorFlow. Part Three takes neural networks in computer vision as its thread and unfolds through three comprehensive cases: recognizing all things, knowing all things, and drawing all things. Readers can thereby build a continuous learning path from traditional machine learning to deep learning and then to practice on vision tasks.

What abilities does the comprehensive case section mainly train?

Part Three takes neural network applications in computer vision problems as its thread and sets up three comprehensive cases: AI recognizing all things, AI knowing all things, and AI drawing all things. Its training focus is to connect the Python programming, machine learning, and deep learning knowledge from the first two parts and go through the complete workflow of data processing, model construction, training, and result analysis. Readers should focus on task decomposition and workflow organization, not merely running ready-made code; only then can they transfer case experience to other vision tasks.

What tools or environment should I install in advance?

Chapter 1 covers Python and development environment setup, and later chapters use common Python machine learning libraries and TensorFlow. It is advisable to install and verify the environment in the book's order: first ensure that the basic Python examples run, then gradually install the machine learning and deep learning libraries. During study, keep a virtual environment or version records so that you can roll back and troubleshoot when problems arise from library version differences. For specific installation steps and version requirements, follow the book's actual instructions.

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About the author

杨柳 Author

Yang Liu is an author credited with works on a remarkably wide range of subjects. Public information about this author is limited, and no confirmed record of nationality, dates of birth and death, or educational background is available. Twenty-five titles are held in the collection, spanning social sciences, culture, programming and design, finance, medicine, and fashion, indicating cross-disciplinary writing interests. Among them are documentary works such as We Under the Sun: Oral Histories of Women with Disabilities, which focuses on marginalized groups and presents individual experience through oral accounts, as well as cultural essay-style writing such as Spring Breeze Blows the Wine Flags of Chang'an. On innovation and finance, the author explores research mechanisms and the integration of technology and finance. The author has also written on applied fields such as Python artificial intelligence development and foreign exchange trading practice, balancing humanistic concern with practical knowledge for different readerships.

郭坦 Author

Guo Tan: publicly available information is limited, and his identity and biographical background remain to be verified. Judging from the works currently on record, Guo Tan's writing spans two markedly different fields: traditional medicine and modern programming technology. One work attributed to him, Shibian Liangfang (Ten Convenient Fine Formulas), belongs to medicine and, judging by its title, appears to be a formula book, possibly collecting medical prescriptions convenient for everyday use, reflecting the compiling interests of traditional medical literature. The other, Python Artificial Intelligence Development from Beginner to Mastery, belongs to programming and design and addresses technical introductory and advanced material for AI development. These two subjects are far apart, and an author covering both traditional Chinese formula books and Python AI development is relatively rare among writers. Owing to the lack of reliable information on the author's life, professional background, and motivations for writing, it is currently difficult to determine any inner connection between the two works, and speculation about his academic lineage or writing process would be inappropriate. Readers and researchers may consult the specific contents of the two works to further understand Guo Tan's writing orientation and actual contributions.

鲁银芝 Author

Lu Yinzhi is a book author about whom limited public information is available. The only confirmed representative work is Python Artificial Intelligence Development from Beginner to Mastery, which is classified under programming and design. Based on the available information, the author writes on the intersection of the Python language and artificial intelligence development, addressing readers who wish to learn the relevant technologies systematically, with content organized along a complete path from introductory basics to more advanced applications. Because the author's dates of birth and death, nationality, educational background, and professional experience are not clearly recorded, this profile cannot offer further biographical details, nor can it assess the author's specific standing or scope of influence in the field. As for subject matter, the author has chosen a technically demanding and rapidly changing area, and the writing tends to be oriented toward practical development, combining language fundamentals with artificial intelligence applications. It may serve as a reference for programming learners exploring this direction.