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TinyML Cookbook

You're reading from TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801814973

Length 344 pages

Edition 1st Edition

Tools

Combine

Concepts

Artificial Intelligence

Author (1):

Gian Marco Iodice

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Getting Started with TinyML

2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER

3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Chapter 4: Voice Controlling LEDs with Edge Impulse

5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano

6. Chapter 6: Building a Gesture-Based Interface for YouTube Playback

7. Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS

8. Chapter 8: Toward the Next TinyML Generation with microNPU

9. Other Books You May Enjoy

Technical requirements

To complete all the practical recipes of this chapter, we will need the following:

An Arduino Nano 33 BLE Sense board
A Raspberry Pi Pico board
Smartphone (Android phone or Apple iPhone)
Micro Universal Serial Bus (USB) cable
1 x half-size solderless breadboard
1 x electret microphone amplifier - MAX9814 (Raspberry Pi Pico only)
11 x jumper wires (Raspberry Pi Pico only)
2 x 220 Ohm resistor (Raspberry Pi Pico only)
1 x 100 Ohm resistor (Raspberry Pi Pico only)
1 x red LED (Raspberry Pi Pico only)
1 x green LED (Raspberry Pi Pico only)
1 x blue LED (Raspberry Pi Pico only)
1 x push-button (Raspberry Pi Pico only)
Laptop/PC with either Ubuntu 18.04+ or Windows 10 on x86-64

The source code and additional material are available in the Chapter04 folder of the GitHub repository (https://github.com/PacktPublishing/TinyML-Cookbook/tree/main/Chapter04).

The rest of the chapter is locked

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Authors (1)

Gian Marco Iodice

Gian Marco Iodice

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.

See other products by Gian Marco Iodice

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