Edge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the architecture, algorithms, and applications of Edge AI, offering insights into regulation and governance. Real-world case studies and practical examples are included, providing readers with the knowledge and tools to harness the transformative power of Edge AI. This book also addresses the ethical considerations and regulatory aspects of deploying AI at the edge.
In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Fundamentals of machine learning and deep learning - p. 15 3. Chapter 3. Edge AI hardware - p. 33 4. Chapter 4. Edge AI edge AI algorithms and techniques - p. 59 5. Chapter 5. Edge AI software development - p. 85 6. Chapter 6. Edge AI applications - p. 105 7. Chapter 7. Edge AI challenges and future directions - p. 127 8. Chapter 8. Case studies and practical examples - p. 151 9. Chapter 9. Ethical considerations - p. 173 10. Chapter 10. Conclusion and future outlook - p. 189 11. Index - p. 195
Edge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the architecture, algorithms, and applications of Edge AI, offering insights into regulation and governance. Real-world case studies and practical examples are included, providing readers with the knowledge and tools to harness the transformative power of Edge AI. This book also addresses the ethical considerations and regulatory aspects of deploying AI at the edge.
In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Fundamentals of machine learning and deep learning - p. 15 3. Chapter 3. Edge AI hardware - p. 33 4. Chapter 4. Edge AI edge AI algorithms and techniques - p. 59 5. Chapter 5. Edge AI software development - p. 85 6. Chapter 6. Edge AI applications - p. 105 7. Chapter 7. Edge AI challenges and future directions - p. 127 8. Chapter 8. Case studies and practical examples - p. 151 9. Chapter 9. Ethical considerations - p. 173 10. Chapter 10. Conclusion and future outlook - p. 189 11. Index - p. 195
