SKU/Artículo: AMZ-B0G82RGTYP

TinyML for Home Security: Practical TinyML Techniques for Building Secure, Intelligent, and Efficient Home Surveillance Systems

Format:

Kindle

Paperback

Detalles del producto

Disponibilidad:
Fuera de stock

Peso con empaque:
0.15 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

TinyML for Home Security is a practical, forward-thinking guide for creating intelligent, low-power home security systems with embedded machine learning. As smart homes become more connected and vulnerable, this book demonstrates how TinyML enables secure, responsive, and privacy-conscious solutions that run directly on resource-constrained devices, rather than relying on constant cloud connectivity.Designed for engineers, developers, makers, and technology enthusiasts, the book bridges the gap between machine learning theory and real-world deployment. Readers will learn how to design, train, and deploy lightweight ML models on microcontrollers to detect events, recognize patterns, and enhance security while keeping power consumption, cost, and latency to a minimum.The book covers fundamental TinyML concepts, embedded hardware considerations, sensor integration, and model optimization techniques with lucid explanations and useful examples. It highlights security-focused use cases like intelligent alerts, environmental monitoring, and intrusion detection, showing how on-device intelligence can enhance dependability, responsiveness, and data privacy.TinyML for Home Security gives you the skills and assurance to build reliable, efficient, and successful systems, whether you're an embedded systems expert hoping to branch out into machine learning, a security-conscious developer investigating edge AI, or a driven enthusiast creating smarter home solutions.

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TinyML for Home Security: Practical TinyML Techniques for Building Secure, Intelligent, and Efficient Home Surveillance Systems

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