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Könyv Tiny Machine Learning: Design Principles and Appli cations Agbotiname Lucky Imoize

Tiny Machine Learning: Design Principles and Appli cations

Szerző: Agbotiname Lucky Imoize
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: John Wiley & Sons Inc
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
49 616 Ft
An expert compilation of on-device training techniques, regulatory frameworks, and ethical considera...

Információk a könyvről

Szerző
Agbotiname Lucky Imoize
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2026
oldal
400
EAN
9781394294541
Enbook ID
46010273
Kiadó
John Wiley & Sons Inc
Súly
666

Teljes leírás

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 schemesIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyMLPractical 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.

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