Raspberry Pi上的人工智能入门指南：2017技术实践

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"Apress.Beginning.Artificial.Intelligence.with.the.Raspberry.Pi.2017" 是一本专为初学者设计的人工智能入门教材，利用Raspberry Pi这台流行的微型计算机平台来探索人工智能技术。作者Donald J. Norris在书中结合实践操作，引导读者了解并掌握人工智能的基本概念和原理。本书适合对编程有一定基础并对人工智能感兴趣的读者，特别是那些想要通过实际项目来学习AI的工程师或学生。该书的主要内容围绕着以下几个方面展开： 1. **人工智能基础知识**：首先，作者会介绍人工智能的基本概念，包括机器学习、深度学习、神经网络等核心理论，让读者对人工智能有个全面的认识。 2. **Raspberry Pi简介**：书中会详细讲解Raspberry Pi的硬件构成、操作系统（如Raspbian）以及如何设置和配置这个小型电脑，以便作为人工智能实验的平台。 3. **编程与算法实现**：通过Python编程语言，读者将学习如何在Raspberry Pi上实现简单的机器学习算法，如线性回归、决策树等，理解这些算法是如何模拟人类思维解决问题的。 4. **案例研究**：书中提供了多个实战项目，如图像识别、语音识别、游戏AI等，帮助读者在实践中掌握人工智能技术的应用。 5. **深度学习入门**：对于有更高追求的读者，书中还会涉及一些基本的深度学习概念，如卷积神经网络（CNN）和循环神经网络（RNN），以及如何使用TensorFlow或Keras这样的库在Raspberry Pi上进行简单模型训练。 6. **版权与许可**：最后，本书还强调了版权法律和许可协议的重要性，确保读者在合法范围内使用和分享教育资源。 "Apress.Beginning.Artificial.Intelligence.with.the.Raspberry.Pi.2017"是一本实用的教程，不仅教授人工智能的基础知识，还通过Raspberry Pi的实际应用，使学习者能够在动手实践中体验人工智能的魅力。无论你是人工智能的新手还是想扩展技能的开发者，这本书都能提供一个宝贵的起点。

■ PrefaCe

I use a variety of languages and applications in the book’s various demonstrations

and projects. The languages used are mainly Python, Prolog, and the Wolfram Language.

Each of these languages brings some unique features that allow the book demonstrations

to be quickly and easily implemented.

The main application that I use is Mathematica, which is a full-featured symbolic

processing program that also happens to be part of the Jessie distribution. Mathematica

is also a commercial program that ordinarily costs hundreds of dollars, but is provided

gratis due to the very generous gift of the Wolfram Corporation and Dr. Stephen Wolfram

(CEO) in particular.

I tried to layout the book in a logical manner by first introducing AI in Chapter 1. It is

difficult to explain AI to people who have never heard of it, although it is often surprising

to inform that them that AI often affects them in their daily lives. I have provided a

considerable amount of detail in the first chapter by trying to define AI and how it is

commonly applied in everyday life situations. It will soon become apparent to you how

invasive AI has become in modern society, whether you like it or not. Please note that I

used the term invasive in a non-derogatory way, simply to point out that AI is commonly

applied in many areas, some of which will surprise you. In addition, I also discuss the

topic of business intelligence (BI), as it is very closely allied to AI and is often the vehicle

through which AI affects most people. Some AI practitioners often refer to BI as simply

AI applied in a business setting. You will learn that it is much more than that, however.

I adopt it because it is a useful simplification.

I next explore some basic AI concepts in Chapter 2. There is initially some discussion

regarding basic logical constructs, as they are important to understand inference, which

is an AI core foundation. Expert knowledge systems are next discussed, which constitute

a major portion of the more general knowledge management systems (KMS)—an

important part of BI. The discussion then turns to machine learning, which is a huge

research area in modern AI. Finally, I conclude the chapter with an introduction to fuzzy

logic (FL), which is thoroughly demonstrated in a later book project.

Chapter 3 shows you how to implement a practical expert system using the Prolog

language. I explore some key Prolog features and explain how this somewhat specialized

language is so useful in implementing AI concepts, without requiring extensive

programing support as would be necessary if general-purpose languages such as C/C++

or Java were used for the same purposes. A simple console question-and-answer program

is used in the practical demonstration.

Chapter 4 focuses on using AI with games. Admittedly, the games are quite simple;

however, the chapter’s goal is to simply demonstrate how AI is incorporated into gaming

logic. These gaming AI concepts may then be easily expanded to handle much more

complex games. I used Python to implement the games, which are controlled through a

traditional text-console interface. Do not expect to see World of Warcraft (WoW)–quality

graphics in this chapter, but rest assured that WoW does use AI in its games.

In Chapter 5, I return to using Prolog to implement some fuzzy logic controls

for a practical project demonstration. There is also a simplified expert rules system

incorporated into the project. A Raspberry Pi system using both temperature and

humidity sensors will control a virtual heating and cooling system.

Chapter 6 introduces the concept of shallow machine learning. A Python program

is created, in which the computer “learns” your favorite color and make “decisions”

regarding color selection. Finally, I close the chapter with a discussion of adaptive

learning, which plays a large role in BI.

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Raspberry Pi上的人工智能入门指南：2017技术实践

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