Embedded AI: A Practical Guide to Building Intelligence on Microcontrollers - David Such - Marjane Mall - Image 1
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Embedded AI: A Practical Guide to Building Intelligence on Microcontrollers - David Such

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Livraison entre le Vendredi 10 juillet 2026 et le Samedi 11 juillet 2026

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Marque : GENERIC
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Plongez dans le monde de l'intelligence artificielle embarquée avec ce guide pratique complet conçu pour les ingénieurs et les passionnés de technologie. À travers 600 pages, David Such vous accompagne dans la conception, l'entraînement et le déploiement de systèmes intelligents sur microcontrôleurs, en abordant l'ensemble de la chaîne technique, d...

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Description produit

Marque
GENERIC
Titre principal
Embedded AI: A Practical Guide to Building Intelligence on Microcontrollers
Editeur
No Starch Press
Type de produit
Paperback
Présentation du livre
Paperback
Release date
10/6/2026 12:00:00 AM
Langue d'origine
English
ISBN
6933595021
Dimensions
9.25 x 0.79 x 7.01 inches
Nombre de pages de livre
600 pages
Langue - Librairie
English
Résumé
A project-driven guide to designing, training, and deploying artificial intelligence directly on embedded hardware, showing how to build intelligent, autonomous systems under real-world constraints.If you already know your way around a microcontroller and want to add embedded AI to it—or you work in ML and you're ready to get your hands on real hardware—this book is for you. It covers the full embedded AI stack, from circuit design and custom PCB fabrication through sensor fusion and signal processing to on-device inference.You'll learn how to wire the sensor, condition the signal, fuse IMU data using complementary filters, Madgwick, Mahony, and Kalman filters, deploy decision trees that run inside the sensor itself, and figure out why your tensor arena is the wrong size. Along the way, you'll tackle exploratory data an*lysis, model quantization, and the debugging realities that documentation never mentions—like what to do when the firmware uploader is fragile and your breadboard connections are dodgy.Working on Arduino (UNO R3 and R4, Nano 33 BLE Sense, Nicla Vision, Nicla Voice), Raspberry Pi Pico 2, and ST evaluation boards, you'll build 25 complete projects, including:Signal generator using PIO and DMA on the Raspberry Pi PicoBattery state-of-charge prediction with Gaussian Process RegressionPerson detection using CNNs on the Nicla VisionOrientation detection using finite state machines running on-sensorSensor fusion filter comparison across four IMUs with static angle testingRobot arm anomaly detection with decision trees on the ISM330BX machine learning coreReal-time audio noise suppression using a GRU neural network on the Pico 2AI MIDI synthesizer with GAN-generated music, capacitive touch keyboard, VS1053b hardware synth, and procedural composition with Markov chains--all on custom PCBsHot word detection on the Nicla Voice using Edge ImpulseBattery monitor and logging shield with BQ24075 charger, fuel gauge, and programmable discharge loadFive custom PCBs a
Auteur(s)
David Such
Date de parution
10/6/2026 12:00:00 AM