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TinyML Cookbook - Second Edition: Combine machine learning with microcontrollers to solve real-world problems 2nd ed. Edition

by Gian Marco Iodice (Author)
4.4 4.4 out of 5 stars 15 ratings
See all formats and editions
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Purchase options and add-ons

Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features
  • Over 20+ new recipes, including recognizing music genres and detecting objects in a scene
  • Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more
  • Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device
Book Description

Discover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.

TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP.

This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you'll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!

What you will learn
  • Understand the microcontroller programming fundamentals
  • Work with real-world sensors, such as the microphone, camera, and accelerometer
  • Implement an app that responds to human voice or recognizes music genres
  • Leverage transfer learning with FOMO and Keras
  • Learn best practices on how to use the CMSIS-DSP library
  • Create a gesture-recognition app to build a remote control
  • Design a CIFAR-10 model for memory-constrained microcontrollers
  • Train a neural network on microcontrollers
Who this book is for

This book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you're an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion.

Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.

Table of Contents
  1. Getting Ready to Unlock ML on Microcontrollers
  2. Unleashing Your Creativity with Microcontrollers
  3. Building a Weather Station with TensorFlow Lite for Microcontrollers
  4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
  5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1
  6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
  7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
  8. Classifying Desk Objects with TensorFlow and the Arduino Nano
  9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

(N.B. Please use the Look Inside option to see further chapters)

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Previous page
  1. ISBN-10
    1837637369
  2. ISBN-13
    978-1837637362
  3. Edition
    2nd ed.
  4. Publisher
    Packt Publishing
  5. Publication date
    November 29, 2023
  6. Language
    English
  7. Dimensions
    9.25 x 7.52 x 1.37 inches
  8. Print length
    664 pages
  9. See all details
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Editorial Reviews

About the Author

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.

Product details

  • Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (November 29, 2023)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 664 pages
  • ISBN-10 ‏ : ‎ 1837637369
  • ISBN-13 ‏ : ‎ 978-1837637362
  • Item Weight ‏ : ‎ 2.51 pounds
  • Dimensions ‏ : ‎ 9.25 x 7.52 x 1.37 inches
  • Customer Reviews:
    4.4 4.4 out of 5 stars 15 ratings

About the author

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Gian Marco Iodice

Gian Marco Iodice

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Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. 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 co-founded the TinyML UK meetup group to encourage knowledge sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.

Customer reviews

4.4 out of 5 stars
4.4 out of 5
15 global ratings
Must read for hardware enthusiast
5 Stars
Must read for hardware enthusiast
If you're into microcontrollers and machine learning like I am, this book is an absolute gem. It's the perfect fusion of both worlds, providing valuable insights on how to leverage machine learning to tackle real-world challenges on power and compute-constrained devices.
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Top reviews from the United States

Jon M.
5.0 out of 5 stars Great upgrade to the first edition
Reviewed in the United States on February 8, 2024
Verified Purchase
This is a really good book for learning TinyML (Ai for the Edge). The instructions are easy to follow, and the board selection is very nice. There were a couple of issues with the instructions, but nothing that was too difficult to get pass. I'm really enjoying going through this book.
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Heena Chouhan
5.0 out of 5 stars Must read for hardware enthusiast
Reviewed in the United States on February 7, 2024
Verified Purchase
If you're into microcontrollers and machine learning like I am, this book is an absolute gem. It's the perfect fusion of both worlds, providing valuable insights on how to leverage machine learning to tackle real-world challenges on power and compute-constrained devices.
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Heena Chouhan
5.0 out of 5 stars Must read for hardware enthusiast
Reviewed in the United States on February 7, 2024
If you're into microcontrollers and machine learning like I am, this book is an absolute gem. It's the perfect fusion of both worlds, providing valuable insights on how to leverage machine learning to tackle real-world challenges on power and compute-constrained devices.
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D. King
5.0 out of 5 stars One of the best technical cookbooks around.
Reviewed in the United States on December 8, 2023
I’ve spent much of the past 7+ years working with various groups of software developers focused on the problem of 'cramming' AI/ML capabilities into various microcontrollers and microprocessors found in all sorts of resource-constrained devices. These ranged from tail tags on cows, gait monitors for Parkinson patients, engines on large scale mining equipment, and video cameras on mini-drones and low earth orbit satellites, to name just a few. During each of these projects a sizeable percentage of our time was spent gathering and assessing various books, papers, research papers and the like in order to address the problem. This is how I came across Gian Marco Iodice’s ‘TinyML Cookbook – both the the 1st edition (April 2022), and now the 2nd edition (November 2023) . If these editions had been published earlier, then they could have served as a valuable resource, saving us time, reducing our false starts, and introducing much more rigor to our development processes.

Like its predecessor, this 2nd edition of the Cookbook has a decidely practical hands-on approach (much like the ‘Make’ books for microcontrollers). Starting with an intro to TinyML, ML, and deep learning (DL), it quickly guides readers through the steps required to setup the microcontroller hardware and software environments used to ‘cook’ the recipes in the remainder of the book. Most recipes in this Cookbook employ the Arduino Nano 33 BLE Sense, the Raspberry Pi Pico, and occasionally the SparkFun Artemis Nano. Likewise, the same dev environment (Arduino IDE or Arduino Web, TensorFlow, Edge Impulse, and TensorFlow Lite) and development procedures are in most of the chapters, taking the reader from initial project statement to prototype completion. Crudely put, for each project the reader starts on the cloud with the goal of building, training and testing the appropriate TensorFlow ML model. Once completed, TensorFlow Lite and quantization are used to create an ML model that can then be deployed on the various microprocessors of interest.

What makes this Cookbook enjoyable is the non-trivial nature of its projects. While some may seem basic at first glance, such as predicting snowfall from sensor data or controlling LEDs with voice commands, diving into the details of these, as well as the other projects, reveals their complexity. These projects can serve as building blocks for more intricate applications.

In the world of Amazon reviews, we often scrutinize 1s and 2s in deciding whether to purchase the product. For the earlier edition, there were no such ratings, just one 4 and the rest 5s (28 of them), with reviewers praising its practicality, readability, and comprehensive coverage. From my own experience, these accolades extend to the 2nd edition.

Who should consider this book? It's not for non-programmers or beginners. Instead, it's ideal for those with ML and programming experience in Python and C/C++ who want to explore tinyML and Edge AI. Conversely, if you're comfortable with microcontrollers and programming but new to ML, this book is an excellent starting point. If you're well-versed in both ML and microcontrollers and seek a deeper understanding of tinyML, this book is a valuable addition to your library. Finally, if you already have the 1st edition, then the upgrade to the new edition is well worth it.
3 people found this helpful
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Samuel de Zoete
5.0 out of 5 stars For true engineers
Reviewed in the United States on January 31, 2024
The book TinyML has a wealth of great learning projects for the hands on engineer and data scientist. It's not your typical run of the mill data science book, it's an in-depth journey through projects into TinyML and deployment of example projects. The book has an intimidating 665 pages, however you can just start with one of the many projects. I would recommend to read chapters one and two as they will teach you the basic and skills you need for each project.

Every project start with a list of prerequisites and optional tools, e.g. book snippet ### To complete all the practical recipes of this chapter, we will need the following:
• An Arduino Nano 33 BLE Sense
• A Raspberry Pi Pico
• A SparkFun RedBoard Artemis Nano (optional)
• A micro-USB data cable
• A USB-C data cable (optional)
• 1 x half-size solderless breadboard (Raspberry Pi Pico only)
• 1 x AM2302 module with the DHT22 sensor (Raspberry Pi Pico only)
• 3 x jumper wires (Raspberry Pi Pico only)
• A laptop/PC with either Linux, macOS, or Windows. ###

Then an overview of the different steps, so you have a good idea of your 'mini' milestones. Then each chapter explains how to reach these milestones, it includes code and images, links to webpages and data downloads, etc. The very first project is already good fun, it's how to bring your own weather station alive!

If you are interested in practical engineering using ML, this is the book...it's a must have!
2 people found this helpful
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Anuj Dutt
5.0 out of 5 stars A great hands-on guide for embedded ML with detailed examples
Reviewed in the United States on January 28, 2024
This book is an invaluable resource for both enthusiasts and professionals interested in integrating machine learning with microcontrollers. This practical guide, focusing on tinyML, is perfect for those with a basic understanding of machine learning and an interest in applying it on microcontrollers like Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.

This book is a comprehensive journey into tinyML, starting with fundamental concepts and advancing to real-world sensor applications. It covers a wide range of topics including weather prediction, voice-controlled LEDs, music genre recognition, object detection, and gesture-based interfaces for YouTube playback. Practical projects using Edge Impulse, TensorFlow Lite for Microcontrollers, and Apache TVM are included, making it an essential read for those looking to explore tinyML applications.

This edition of the book stands out with its hands-on approach and detailed recipes that guide the reader through various tinyML implementations. It's particularly useful for readers looking to apply machine learning in memory-constrained environments, offering insights into model optimization and deployment strategies.

Whether you're a beginner or looking to expand your ML knowledge, this book's real-world examples and end-to-end project guidance make it a must-read for anyone venturing into the field of tinyML.
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Top reviews from other countries

cs
4.0 out of 5 stars Overall review
Reviewed in Canada on April 1, 2024
Verified Purchase
cons:
- figures/plots are printed in black and white, making it hard to differentiate between multiple classes of data on a scatter plot.
- no wrapping (bubble wraps, etc.) on the item. comes straight in a cardboard box.

pros:
- good introductory book for anyone who's interested in this stuff. second edition (which is this one) has a lot more in it than the first ed, so i'd recommend getting this one.
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