Chapter 1 Introduction 1
1.1 Overview 1
1.2 Development History 3
1.3 Neural Network Model 6
1.4 Popular Neural Network 7
1.4.1 Convolutional Neural Network 7
1.4.2 Recurrent Neural Network 8
1.4.3 Reinforcement Learning 9
1.5 Neural Network Classification 9
1.5.1 Supervised learning 10
1.5.2 Semi-supervised learning 10
1.5.3 Unsupervised learning 11
1.6 Neural Network Operation 11
1.6.1 Training 11
1.6.2 Inference 12
1.7 Application Development 12
1.7.1 Business Planning 14
1.7.2 Network Design 14
1.7.3 Data Engineering 14
1.7.4 System Integration 15
Exercise 16
Chapter 2 Neural Network 17
2.1 Convolutional Layer 19
2.2 Activation Layer 20
2.3 Pooling Layer 21
2.4 Batch Normalization 22
2.5 Dropout Layer 22
2.6 Fully Connected Layer 23
Exercise 24
Chapter 3 Machine Vision 25
3.1 Object Recognition 25
3.2 Feature Matching 27
3.3 Facial Recognition 28
3.4 Gesture Recognition 30
3.5 Machine Vision Applications 31
3.5.1 Medical Diagnosis 31
3.5.2 Retail Applications 32
3.5.3 Airport Security 33
Exercise 34
Chapter 4 Natural Language Processing 35
4.1 Neural Network Model 36
4.1.1 Convolutional Neural Network 36
4.1.2 Recurrent Neural Network 37
4.1.2.1 Long Short-Term Memory Network 38
4.1.3 Recursive Neural Network 39
4.1.4 Reinforcement Learning 40
4.2 Natural Language Processing Applications 41
4.2.1 Virtual Assistant 41
4.2.2 Language Translation 42
4.2.3 Machine Transcription 43
Exercise 45
Chapter 5 Autonomous Vehicle 46
5.1 Levels of Driving Automation 46
5.2 Autonomous Technology 48
5.2.1 Computer Vision 48
5.2.2 Sensor Fusion 49
5.2.3 Localization 51
5.2.4 Path Planning 52
5.2.5 Drive Control 52
5.3 Communication Strategies 53
5.3.1 Vehicle-to-Vehicle Communication 54
5.3.2 Vehicle-to-Infrastructure Communication 54
5.3.3 Vehicle-to-Pedestrian Communication 55
5.4 Law Legislation 56
5.4.1 Human Behavior 57
5.4.2 Lability 57
5.4.3 Regulation 58
5.5 Future Challenges 58
5.5.1 Road Rules Variation 58
5.5.2 Unified Communication Protocol 58
5.5.3 Safety Standard and Guideline 59
5.5.4 Weather/Disaster 59
Exercise 60
Chapter 6 Drone 61
6.1 Drone Design 61
6.2 Drone Structure 62
6.2.1 Camera 63
6.2.2 Gyro Stabilization 63
6.2.3 Collision Avoidance 64
6.2.4 Global Positioning System 64
6.2.5 Sensors 64
6.3 Drone Regulation 65
6.3.1 Recreational Rules 65
6.3.2 Commercial Rules 66
6.4 Applications 66
6.4.1 Infrastructure Inspection 66
6.4.2 Civil Construction 67
6.4.3 Agriculture 68
6.4.4 Emergency Rescue 69
Exercise 70
Chapter 7 Healthcare 71
7.1 Telemedicine 71
7.2 Medical Diagnosis 72
7.3 Medical Imaging 73
7.4 Smart Medical Device 74
7.5 Electronic Health Record 76
7.6 Medical Billing 77
7.7 Drug Development 78
7.8 Clinical Trial 79
7.9 Medical Robotics 80
7.10 Elderly Care 81
7.11 Future Challenges 82
Exercise 84
Chapter 8 Finance 85
8.1 Fraud Prevention 85
8.2 Financial Forecast 88
8.3 Stock Trading 89
8.4 Banking 91
8.5 Accounting 94
8.6 Insurance 95
Exercise 96
Chapter 9 Retail 97
9.1 E-Commerce 98
9.2 Virtual Shopping 100
9.3 Product Promotion 102
9.4 Store Management 103
9.5 Warehouse Management 104
9.6 Inventory Management 106
9.7 Supply Chain 108
Exercise 110
Chapter 10 Manufacturing 111
10.1 Defect Detection 112
10.2 Quality Assurance 113
10.3 Production Integration 114
10.4 Generative Design 115
10.5 Predictive Maintenance 117
10.6 Environment Sustainability 118
10.7 Manufacturing Optimization 119
Exercise 121
Chapter 11 Agriculture 122
11.1 Crop and Soil Monitoring 123
11.2 Agricultural Robot 125
11.3 Pest Control 126
11.4 Precision Farming 127
Exercise 129
Chapter 12 Smart City 130
12.1 Smart Transportation 131
12.2 Smart Parking 132
12.3 Waste Management 133
12.4 Smart Grid 134
12.5 Environmental Conservation 135
Exercise 137
Chapter 13 Government 138
13.1 Information Technology 140
13.2 Human Service 141
13.3 Law Enforcement 144
13.3.4 Augmenting Human Movement 147
13.4 Homeland Security 147
13.5 Legislation 149
13.6 Ethics 152
13.7 Public Perspective 155
Exercise 159
Chapter 14 Computing Platform 160
14.1 Central Processing Unit 160
14.1.1 System Architecture 161
14.1.2 Advanced Vector Extension 164
14.1.3 Math Kernel Library for Deep Neural Network 165
14.2 Graphics Processing Unit 165
14.2.1 Tensor Core Architecture 167
14.2.2 NVLink2 Configuration 167
14.2.3 High Bandwidth Memory 169
14.3 Tensor Processing Unit 170
14.3.1 System Architecture 170
14.3.2 Brain Floating Point Format 171
14.3.3 Cloud Configuration 172
14.4 Neural Processing Unit 173
14.4.1 System Architecture 173
14.4.2 Deep Compression 174
14.4.3 Dynamic Memory Allocation 174
14.4.4 Edge AI Server 175
Exercise 176
Appendix A Kneron Neural Processing Unit 178
Appendix B Object Detection (Overview) 179
B.1 Kneron Environment Setup 179
B.2 Python Installation 180
B.3 Library Installation 184
B.4 Driver Installation 185
B.5 Model Installation 186
B.6 Image/Camera Detection 186
B.7 Yolo Class List 190
Appendix C Object Detection - Hardware 192
C.1 Library Setup 192
C.2 System Parameters 193
C.3 NPU Initialization 194
C.4 Image Detection 195
C.5 Camera Detection 197
Appendix D Hardware Transfer Mode 199
D.1 Serial Transfer Mode 199
D.2 Pipeline Transfer Mode 201
D.3 Parallel Transfer Mode 203
Appendix E Object Detection – Software (Optional) 205
E.1 Library Setup 205
E.2 Image Detection 207
E.3 Video Detection 208
Reference 211
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UNDERSTANDING ARTIFICIAL INTELLIGENCE: FUNDAMENTALS AND APPLICATIONS的圖書 |
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UNDERSTANDING ARTIFICIAL INTELLIGENCE: FUNDAMENTALS AND APPLICATIONS 作者：LIU(劉峻誠)、LAW(羅明健)、LAW 出版社：全華 出版日期：2022-12-01 |
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圖書介紹 - 資料來源：TAAZE 讀冊生活 評分：
圖書名稱：UNDERSTANDING ARTIFICIAL INTELLIGENCE: FUNDAMENTALS AND APPLICATIONS
- Focuses on artificial intelligence applications in different industries and sectors
- Traces the history of neural networks and explains popular neural network architectures
- Covers AI technologies, such as Machine Vision (MV), Natural Language Processing (NLP), and Unmanned Aerial Vehicles (UAV)
- Describes various artificial intelligence computational platforms, including Google Tensor Processing Unit (TPU) and Kneron Neural Processing Unit (NPU)
- Highlights the development of new artificial intelligence hardware and architectures
Understanding Artificial Intelligence
Provides students across majors with a clear and accessible overview of new artificial intelligence technologies and applications
Artificial intelligence (AI) is broadly defined as computers programmed to simulate the cognitive functions of the human mind. In combination with the Neural Network (NN), Big Data (BD), and the Internet of Things (IoT), artificial intelligence has transformed everyday life: self-driving cars, delivery drones, digital assistants, facial recognition devices, autonomous vacuum cleaners, and mobile navigation apps all rely on AI to perform tasks. With the rise of artificial intelligence, the job market of the near future will be radically different???many jobs will disappear, yet new jobs and opportunities will emerge.
Understanding Artificial Intelligence: Fundamentals and Applications covers the fundamental concepts and key technologies of AI while exploring its impact on the future of work. Requiring no previous background in artificial intelligence, this easy-to-understand textbook addresses AI challenges in healthcare, finance, retail, manufacturing, agriculture, government, and smart city development. Each chapter includes simple computer laboratories to teach students how to develop artificial intelligence applications and integrate software and hardware for robotic development. In addition, this text:
- Focuses on artificial intelligence applications in different industries and sectors
- Traces the history of neural networks and explains popular neural network architectures
- Covers AI technologies, such as Machine Vision (MV), Natural Language Processing (NLP), and Unmanned Aerial Vehicles (UAV)
- Describes various artificial intelligence computational platforms, including Google Tensor Processing Unit (TPU) and Kneron Neural Processing Unit (NPU)
- Highlights the development of new artificial intelligence hardware and architectures
Understanding Artificial Intelligence: Fundamentals and Applications is an excellent textbook for undergraduates in business, humanities, the arts, science, healthcare, engineering, and many other disciplines. It is also an invaluable guide for working professionals wanting to learn about the ways AI is changing their particular field.
作者簡介：
ALBERT LIU, PhD, is the CEO of Kneron. He is also an Adjunct Associate Professor at National Tsing Hua University, National Chiao Tung University, and National Cheng Kung University. He is a head researcher on algorithms software projects; proposed, implemented and published over 15 IEEE papers, including one ICCAD best paper nomination, one ICCD best paper nomination and one IBM contest award. He is an IEEE Senior Member.
OSCAR MING KIN LAW, PhD, is a senior staff member of physical design at Qualcomm Inc. He has 20+ years semiconductor industry background on CPU, GPU, FPGA and mobile design. He is a Senior Member of IEEE and a Member of Professional Engineers in Ontario.
IAIN LAW is a rising senior at Canyon Crest Academy. He is passionate about artificial intelligence and its impacts towards the youth, working on several artificial intelligence projects like the LEGO smart robot and DJI Tello smart drone. Also, he has helped to run the AI Workshop under YELP program, a summit that introduces new artificial intelligence technology, deep learning and shows the public on how to deal with the implications of artificial intelligence challenges in the future.
目錄
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Development History 3
1.3 Neural Network Model 6
1.4 Popular Neural Network 7
1.4.1 Convolutional Neural Network 7
1.4.2 Recurrent Neural Network 8
1.4.3 Reinforcement Learning 9
1.5 Neural Network Classification 9
1.5.1 Supervised learning 10
1.5.2 Semi-supervised learning 10
1.5.3 Unsupervised learning 11
1.6 Neural Network Operation 11
1.6.1 Training 11
1.6.2 Inference 12
1.7 Application Development 12
1.7.1 Business Planning 14
1.7.2 Network Design...
1.1 Overview 1
1.2 Development History 3
1.3 Neural Network Model 6
1.4 Popular Neural Network 7
1.4.1 Convolutional Neural Network 7
1.4.2 Recurrent Neural Network 8
1.4.3 Reinforcement Learning 9
1.5 Neural Network Classification 9
1.5.1 Supervised learning 10
1.5.2 Semi-supervised learning 10
1.5.3 Unsupervised learning 11
1.6 Neural Network Operation 11
1.6.1 Training 11
1.6.2 Inference 12
1.7 Application Development 12
1.7.1 Business Planning 14
1.7.2 Network Design...
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