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Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico

Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse

Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU

Description

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.

Who is this book for?

This book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.

What you will learn

Understand the relevant microcontroller programming fundamentals

Work with real-world sensors such as the microphone, camera, and accelerometer

Run on-device machine learning with TensorFlow Lite for Microcontrollers

Implement an app that responds to human voice with Edge Impulse

Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense

Create a gesture-recognition app with Raspberry Pi Pico

Design a CIFAR-10 model for memory-constrained microcontrollers

Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM

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Client Amazon Apr 14, 2022

We have been tinkering and testing ideas in the when it comes to "Physical Computing" for a long time. We have also been looking for a book we can use as a reference standard. It has been a long time coming. We have finally found the perfect book. TinyML Cookbook: Combine artificial intelligence.... is the best I have seen thus far on the market. This book is pedagogically sound. It's designed in such a way that even upper elementary level learners are able to follow the recipes and conduct the hands-on inquiry-based activities. We have conducted many of the activities using both the Arduino Nona 33 and the Raspberry Pi Pico. This is an excellent book which can be used to introduce learners to embedded/edge/cloud computing, microcontroller/edgeware, TinyML, Machine Learning-ML, Deep Learning-DL, Artificial Intelligence-AI, Internet of Things-IoT. Every STEM educator/tinkerer on Earth needs to have a copy of this book. We are safely guarding our copy close to our chest in the Alchemist Club StudiosDr. RonelusSTEM Learning ScientistNew York City Board of Education

Amazon Verified review

Jack Raifer Apr 27, 2022

When I got this book, I thought it would be like a tiny reference guide for ML applications, lo and behold, I got happily enticed into the world of IoT devices and how to get around their processing limitations to implement Machine Learning in them. A happy surprise and now one of my favorite books.

tina Jul 20, 2022

Background: I've been involved with TinyML for over 6 years through working at Qeexo, and was at the inaugural TinyML Meetup at Qualcomm in the Silicon Valley. I also read Pete and Daniel's "TinyML: Machine Learning with TensorFlow Lite".That said, I found the TinyML Cookbook... well, very "cookbook"-like. Besides a pretty general (but good) intro of TinyML not unlike my own TinyML pitch, it comes with very detailed, step-by-step instructions and pre-prepared code on GitHub, so it's super easy to get started, even if you're not a coder or embedded engineer. (I am neither, but I do have a CS degree from a long time ago.)This Cookbook is very well-structured, and broken down into individual projects and categories. One can select the tools and hardware one is interested in, and start there, without having to go through the entire book, similar to how you can pick out one dish to cook from a cookbook. Full disclosure: I didn't do every single project, just selected a couple that I already have the hardware for (Arduino Nano 33 BLE Sense, Raspberry Pi Pico).Chapter 8 on the Arm microNPU is actually the most interesting to me as the latest and greatest tech in TinyML, however:"Arm Ethos-U55 is the first microNPU designed by Arm to extend the ML capabilitiesof Cortex-M-based microcontrollers. Unfortunately, there is no hardware availabilitywith this new processor at the time of writing. However, Arm offers a free Fixed VirtualPlatform (FVP) based on the Arm Corstone-300 system to quickly experiment with MLmodels on this processor without the need for physical devices."So, I was not able to test with actual hardware.Overall, it was quite fun to try some new things out. One thing I wish it had is more room for experimenting or expanding upon the current project - pointers on how to change the current code to achieve different results, or other applications and real-life use cases of these "demo" projects. (However, I understand that's not really in the scope of a "cookbook" - one usually doesn't just experiment with random ingredients to see how a dish turns out differently.)Fun read!Anyway,

Venkat Rangan May 02, 2022

The book takes you from developing a super simple Arduino based neural network on a tiny CortexM processor, through the data collection and associated flow to running a neural network on an Ethos NPU accelerator on Qemu. The scope of this book took my breath away!Every chapter is in cookbook style so it encourages learning by doing rather than a formulaic approach (I prefer to learn by doing). There are a wide variety of examples including weather prediction, keyword spotting, vision and using TensorFlow and Edge Impulse. All code is on Github, python based for the most part and easy to dig into and learn from as there is detailed explanation of each step. I think this book significantly reduces the barriers for tinyML by demonstration.Am looking forward to going through the book again, far more slowly and experimentally with HW in tow.This cookbook has some good recipes!

Guangping zhang May 06, 2022

TinyML is a kind of machine learning (ML), it makes AI work with extremely low-powereddevices, such as microcontrollers.This book introduces applications of multidisciplinary fields, such as Arduino Nano 33 BLE Sense and Raspberry Pi Pico.Also the book discusses microcontrollers, such as controlling the LED state with GPIO and a push-button and supplying power to microcontrollers with batteries. After that, the book covers recipes relating to temperature, humidity, and the three V (voice, vision, and vibration) sensors to gain the necessary skills to implement end-to-end smart applications in different scenarios.Then the author introduces how to build tiny models for memory-constrained microcontrollers.Finally, the book discusses two of the most recent technologies, microTVM and microNPU, which will help you step up your TinyML game.The book covers the most interesting fields of TinyML, I suggest you read it and have a good start.

TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

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Chapter 2: Prototyping with Microcontrollers

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