本書是創新方法工作專項項目「科學思維、科學方法在高等學校教學創新中的應用與實踐」的主要研究成果之一——FKM教學法的研究與實踐。 本書既有電腦基礎理論知識，包括Computer Basics and Digitization、Computer Hardware、Computer Software、Operating System、Networks、Python Programming Language；也有對人工智慧發展前沿的介紹，包括Artificial Intelligence Basic、Deep Learning以及Large Models and Large Multimodal Models。 本書開篇給出書中所有知識內容的邏輯結構圖，每章均有知識邏輯結構圖，相關知識點附有KM圖，在教學上實現「薄-厚-薄」及反饋（Feedback）的教學迴路，力圖在內容、闡述等方面形成新的模式。 本書適合作為高等院校「大學電腦基礎」課程的雙語教材和留學生教材，也適合作為學習電腦及英語的入門參考書。
作者介紹
編者:張桃紅//卓君寶//姚琳//何傑//萬亞東等|責編:文怡//李曄
目錄
Chapter 1 Computer Basics and Digitization 1.1 Computer Basics 1.1.1 An Overview of the Computer Development 1.1.2 The Digital Revolution 1.1.3 Computer Types and Uses 1.2 Digital Data Representation 1.2.1 An Overview of the Digital Data Representation 1.2.2 Numbering Systems 1.2.3 More Data Representations 1.3 Information Security 1.3.1 An Overview of Information Security 1.3.2 Information Security Techniques 18 Exercises Chapter 2 Computer Hardware 2.1 An Overview of the Computer Hardware 2.2 Microprocessors 2.2.1 Microprocessors Basics 2.2.2 Processor Logic 2.2.3 Microprocessor』s Performance 2.2.4 GPU 2.3 Memory 2.3.1 Random Access Memory 2.3.2 Read-only Memory 2.3.3 Cache Memory 2.3.4 Virtual Memory 2.4 Storage Devices 2.4.1 Storage Basics 2.4.2 Magnetic Storage Technology 2.4.3 Optical Storage Technology 2.4.4 Solid State Storage Technology 2.5 Input and Output Devices 2.5.1 Input Devices 2.5.2 Output Devices 2.5.3 Installing Peripheral Devices 49 Exercises Chapter 3 Computer Software 3.1 An Overview of the Computer Software 3.1.1 Software Aspects 3.1.2 Software Development 3.2 Software Categories 3.2.1 System Software 3.2.2 Application Software 3.2.3 Development Software 3.3 Installing Software 3.3.1 Installation on Windows 3.3.2 Installation on UNIX/Linux 3.4 Security Software 68 Exercises Chapter 4 Operating System 4.1 An Overview of the Operating System 4.2 Operating System Basics 4.2.1 Operating System Activities 4.2.2 User Interfaces
4.2.3 The Boot Process 4.3 Types of Operating Systems 4.4 File Management 4.4.1 File Basics 4.4.2 Application-based File Management 4.4.3 File Backup92 Exercises Chapter 5 The Networks 5.1 An Overview of the Networks 5.2 Network Devices 5.2.1 Wired Devices 5.2.2 Wireless Devices 5.2.3 Transmission Media 5.2.4 Network Topologies 5.3 LAN 5.3.1 Communications Protocols 5.3.2 Network Setup 5.4 Internet 5.4.1 Internet Basics 5.4.2 Internet Access 5.4.3 Internet Services 5.5 The Web and E-mail 5.6 The Network Security138 Exercises Chapter 6 Python Programming Language 6.1 An Overview of Python Programming Language 6.1.1 History of Python Programming Language 6.1.2 The Features of Python Programming Language 6.1.3 Development Environments 6.2 Basics of Python Programming Language 6.2.1 Python Program Overview 6.2.2 Objects 6.2.3 Expressions 6.2.4 Statements 6.2.5 Functions & Modules 6.3 Python Flow Control 6.3.1 Sequence Structure 6.3.2 Selection Structure 6.3.3 Loop Structure 6.4 Data Type 6.4.1 List 6.4.2 Tuples 6.4.3 Strings 6.4.4 Sets 6.4.5 Dictionary 6.5 Graphics 6.5.1 Graphic Rendering Based on Tkinter 6.5.2 Turtle Drawing Based on Turtle Module 6.5.3 Draw a Square 6.5.4 Draw a Polygon 6.5.5 Draw a Circular Spiral Chapter 7 Artificial Intelligence Basic
7.1 An Overview of AI 7.1.1 Definition of Artificial Intelligence 7.1.2 Thinking and Intelligence 7.1.3 Turing Test 7.1.4 History of Artificial Intelligence 7.2 Application and Methods 7.2.1 Automatic Reasoning 7.2.2 Neural Computation 7.2.3 Knowledge Representation 7.2.4 Uncertainty Reasoning 7.3 The Recent Development of Artificial Intelligence 7.3.1 Computer Games 7.3.2 Expert System 7.3.3 Computer Vision 7.3.4 Natural Language Processing 7.3.5 Bioinformatics 7.3.6 New Material Discovery Chapter 8 Deep Learning 8.1 Machine Learning and Artificial Neural Networks 8.1.1 Machine Learning 8.1.2 Artificial Neural Networks 8.2 Introduction to Deep Learning 8.2.1 Layers in Deep Learning Networks 8.2.2 Weights in Deep Learning Networks 8.3 Deep Learning Models 8.3.1 Convolutional Neural Network (CNN) 8.3.2 Recurrent Neural Network (RNN) 8.3.3 Recurrent Long Short-Term Memory Network (LSTM) 8.3.4 Transformer and ViT 8.3.5 Generative Adversarial Network (GAN) Chapter 9 Large Models and Large Multimodal Models 9.1 An Overview of Large Model 9.1.1 Definition of Large Model 9.1.2 History of Large Models 9.1.3 Application Area of Large Models 9.1.4 Future of Large Models 9.2 Large Language Models 9.2.1 GPT (Generative Pre-trained Transformer) 9.2.2 BERT 9.2.3 ChatGPT & DeepSeek 9.3 Large Multimodal Models 9.3.1 Concepts of Large Multimodal Models 9.3.2 Multimodal Data 9.3.3 Fuse Inside Multimodal 9.4 Core Technologies of Large Multimodal Models 9.4.1 Pretraining Tasks 9.4.2 Prompt Learning 9.4.3 In-Context Learning 9.5 Application of Large Multimodal Models 9.5.1 Generative Capabilities of Large Multimodal Models
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