AI and Deep Learning with Arduino and Raspberry Pi: A Hands-On Guide to Smart IoT Projects

• From Sensor to Cloud: Build Real, Working Edge-AI Devices With TinyML, Computer Vision, and Voice Recognition

  For most of the last decade, Arduino books stopped at blinking LEDs, and Raspberry Pi books stopped at web servers — while "AI" lived somewhere else entirely, behind a cloud API. That's no longer how smart products get built. Today's security cameras detect people on the chip inside them. Factory sensors flag a failing bearing before the vibration ever reaches a server. Smart irrigation systems make decisions in milliseconds, with no internet connection required. This is edge AI, and this book teaches you to build it, one working project at a time.

  AI and Deep Learning with Arduino and Raspberry Pi is a hands-on, project-based guide to running real machine learning models directly on the two most accessible platforms in electronics. Every chapter after the foundations builds something physical and testable, so that by the end you're not just following instructions — you're able to design your own devices.

  What makes this book different: Most books pick a lane — Arduino or Raspberry Pi, hardware or machine learning, theory or projects. This one is deliberately built across all three, because that's how real products get made. Sensor nodes run on microcontrollers for battery life; gateways and cameras run on small Linux computers for horsepower, and learning how they talk to each other is how the industry actually works. Every model here is trained, converted, and deployed to run on the device itself, using the TinyML and edge-AI techniques central to modern IoT and embedded product design. Every project ships with a complete parts list, a wiring diagram, working code, and a troubleshooting section — the parts most tutorials skip and most beginners get stuck on.

  Inside, you'll find:

• Foundations: why edge AI matters, a full Arduino vs. Raspberry Pi hardware comparison, and how to set up a complete development environment
• Core skills: sensors and actuators, C++ on Arduino, Python on Raspberry Pi, and a from-scratch machine learning crash course with an introduction to TinyML
• Six hands-on builds: a gesture-controlled smart switch, a vibration-based predictive maintenance monitor, a smart agriculture soil and climate sensor node, a real-time object detection security camera, an AI voice-controlled home assistant, and a smart people-counting system
• Deployment: connecting edge devices to the cloud, model optimization and security, and taking a project from prototype to shippable product
• Reference material: a complete parts-sourcing appendix, a troubleshooting quick reference, a code-pattern cheat sheet, a glossary, and 22 cited sources

Who this book is for: you should be comfortable with basic programming concepts — variables, loops, functions — but no prior machine learning experience is required; Chapter 6 builds it from the ground up. Students, hobbyists, engineers moving into IoT, and educators looking for a semester's worth of lab projects will all find a direct path through this material. You don't need to own every piece of hardware to benefit from every chapter — the reasoning, wiring diagrams, and code are written to be understood even before the parts are in your hands.

Written by Dr Rajesh Dey (Principal, Camellia Institute of Engineering & Technology, and Director, Electrovent LLP) with contributing authors Ashutosh Rana (enterprise AI consultant based in Minneapolis), Dr. Mudassir Khan (Assistant Professor, King Khalid University, and holder of seventeen patents), and Dr. Rupali Mahajan (Professor and Associate Dean for Research, Vishwakarma Institute of Technology, author of ten books). This book pairs practical, tested engineering with real academic and industry expertise.

Stop reading about edge AI. Start building it.