作者：Daniel Situnayake，Je
页数：24,487页
出版社：东南大学出版社
出版日期：2024
ISBN：9787576613001
高清校对版pdf（带目录）
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内容简介
本书提供了一个端到端的框架，用于使用边缘AI解决现实世界中的工业、商业、科学问题。书中将探索整个过程的每个阶段，从数据收集到模型优化，再到调优和测试，学习如何设计和支持边缘AI与嵌入式机器学习产品。
作者简介
丹尼尔·斯托扬卡（Daniel Situnayake），Edge Impulse的机器学习主管，领导嵌入式机器学习研发。
珍妮·普伦基特（Jenny Plunkett），Edge Impulse的高级开发者关系工程师，也是技术宣讲者、开发者布道师和技术内容创作者。
目录
Foreword
Preface
1.A Brief Introduction to Edge AI
Defining Key Terms
Embedded
The Edge (and the Internet of Things)
Artificial Intelligence
Machine Learning
Edge AI
Embedded Machine Learning and Tiny Machine Learning
Digital Signal Processing
Why Do We Need Edge AI?
To Understand the Benefits of Edge AI, Just BLERP
Edge AI for Good
Key Differences Between Edge AI and Regular AI
Summary
2.Edge AI in the Real World
Common Use Cases for Edge AI
Greenfield and Brownfield Projects
Real-World Products
Types of Applications
Keeping Track of Objects
Understanding and Controlling Systems
Understanding People and Living Things
Transforming Signals
Building Applications Responsibly
Responsible Design and AI Ethics
Black Boxes and Bias
Technology That Harms, Not Helps
Summary
3.The Hardware of Edge AI
Sensors, Signals, and Sources of Data
Types of Sensors and Signals
Acoustic and Vibration
Visual and Scene
Motion and Position
Force and Tactile
Optical, Electromagnetic, and Radiation
Environmental, Biological, and Chemical
Other Signals
Processors for Edge AI
Edge AI Hardware Architecture
Microcontrollers and Digital Signal Processors
System-on-Chip
Deep Learning Accelerators
FPGAs and ASICs
Edge Servers
Multi-Device Architectures
Devices and Workloads
Summary
Preface
1.A Brief Introduction to Edge AI
Defining Key Terms
Embedded
The Edge (and the Internet of Things)
Artificial Intelligence
Machine Learning
Edge AI
Embedded Machine Learning and Tiny Machine Learning
Digital Signal Processing
Why Do We Need Edge AI?
To Understand the Benefits of Edge AI, Just BLERP
Edge AI for Good
Key Differences Between Edge AI and Regular AI
Summary
2.Edge AI in the Real World
Common Use Cases for Edge AI
Greenfield and Brownfield Projects
Real-World Products
Types of Applications
Keeping Track of Objects
Understanding and Controlling Systems
Understanding People and Living Things
Transforming Signals
Building Applications Responsibly
Responsible Design and AI Ethics
Black Boxes and Bias
Technology That Harms, Not Helps
Summary
3.The Hardware of Edge AI
Sensors, Signals, and Sources of Data
Types of Sensors and Signals
Acoustic and Vibration
Visual and Scene
Motion and Position
Force and Tactile
Optical, Electromagnetic, and Radiation
Environmental, Biological, and Chemical
Other Signals
Processors for Edge AI
Edge AI Hardware Architecture
Microcontrollers and Digital Signal Processors
System-on-Chip
Deep Learning Accelerators
FPGAs and ASICs
Edge Servers
Multi-Device Architectures
Devices and Workloads
Summary
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