相關主題
商品描述
An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development
In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design.
Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications.
Additional topics covered in the book include:
- A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes
- Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML
- Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis
Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.
商品描述(中文翻譯)
針對 TinyML 設計與開發的設備內訓練技術、法規框架及倫理考量的專家彙編
在Tiny Machine Learning: Design Principles and Applications中，一組傑出的研究者提供了有關 Tiny Machine Learning (TinyML) 的關鍵概念、設計原則、應用及相關議題的全面討論。專家貢獻者介紹了一種新的低功耗資源，為物聯網 (IoT) 設備提供廣泛的應用，並進行系統與演算法的共同設計。
Tiny Machine Learning 探討了 TinyML 的範式與促進因素、TinyML 在異常檢測中的應用，以及 TinyML 下的學習全景。讀者將會找到有關 TinyML 設備與工具、物聯網微控制器中的功耗與記憶體，以及針對 TinyML 的輕量級框架的解釋。本書還描述了針對即時與環境應用的 TinyML 技術。
本書涵蓋的其他主題包括：
- 對 TinyML 設備的安全性與隱私技術的徹底介紹，包括新穎安全方案的實施
- 對物聯網微控制器 (MCUs) 中的功耗與記憶體的深入探討，包括嵌入 TinyML 的超低功耗智慧物聯網設備
- 針對微控制器進行數據提取與合成的 TinyML 研究的實用討論
本書非常適合業界及學術研究者、科學家和工程師，Tiny Machine Learning 也將使對機器學習感興趣的講師和研究生受益。
作者簡介
Agbotiname Imoize is a Lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. He is a Fulbright Fellow, the Vice Chair of the IEEE Communication Society Nigeria chapter, and a Senior Member of IEEE.
Dinh-Thuan Do, PhD, is an Assistant Professor with the School of Engineering at the University of Mount Union, USA. He is an editor of IEEE Transactions on Vehicular Technology and Computer Communications. He is a Senior Member of IEEE.
Houbing Herbert Song, PhD, IEEE Fellow, is a Professor in the Department of Information Systems, and the Department of Computer Science and Electrical Engineering and Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab) at the University of Maryland, Baltimore County. He is also Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics.
作者簡介(中文翻譯)
Agbotiname Imoize 是尼日利亞拉哥斯大學電氣與電子工程系的講師。他是富布賴特學者，IEEE通訊學會尼日利亞分會的副主席，以及IEEE的資深會員。
Dinh-Thuan Do, PhD, 是美國蒙特聯大學工程學院的助理教授。他是IEEE車輛技術期刊和計算機通信期刊的編輯，也是IEEE的資深會員。
Houbing Herbert Song, PhD, IEEE Fellow, 是馬里蘭大學巴爾的摩縣分校資訊系、計算機科學與電氣工程系的教授，並擔任網絡全球安全與優化實驗室（SONG Lab）的主任。他也是IEEE工業資訊期刊的共同主編。
