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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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Paperback book shipped to your preferred address

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Frequently bought together

$44.99

Machine Learning with PyTorch and Scikit-Learn

$54.99

$49.99

Total $ 149.97

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.

Amazon Verified review

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!

Amazon Verified review

Colin Osborne Apr 12, 2022

I found this book really well written, with a refreshing clarity of wording, nice accessible explanations of each new topic or terminology, and a range of fun and interesting example TinyML applications to learn from by doing.The book is nicely structured and well paced, giving everything from the context, purpose, software instructions and links, and even the hardware shopping list with online links to help get everything needed to try out the examples.The chapters go from very first introduction through to giving several different fully explained and easy to follow examples to implement TinyML systems for yourself.Chapter list:1. Getting Started with TinyML2. Prototyping with Microcontrollers3. Building a Weather Station with TensorFlow Lite for Microcontrollers4. Voice Controlling LEDs with Edge Impulse5. Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano6. Building a Gesture-Based Interface for YouTube Playback7. Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS8. Toward the Next TinyML Generation with microNPUMost of the examples show how to run on one of the following micro-controller boards:* Arduino Nano 33 BLE Sense* Raspberry Pi PicoThere are also a couple of examples running on a virtual platform instead of physical hardware.(Somewhat inevitably for any non-trivial technical exercises, I did spot a few minor errata. The publisher does helpfully provide a feedback form, and has said the errata will be published on the GitHub to help other readers, so please do look for that or ask the publisher if you need to.)To run the examples on real hardware there are some electronic components required, including the micro-controller itself but then also solder-less breadboards, sensors, jumper wires, battery holders and a few basic components such as resistors or LEDs.The link to the shopping list on publisher’s GitHub page is given in the book, but if you want to save yourself some time and order at the same time as the book, I summarise the shopping list as follows:* Arduino Nano 33 BLE Sense with headers: https://www.amazon.co.uk/Arduino-NANO-SENSE-headers-mounted/dp/B07WXKDVTL* Raspberry Pi Pico with headers: https://www.amazon.co.uk/Raspberry-Soldered-Microcontroller-Development-Pre-soldered/dp/B08ZSMZ8FT* Electronics Component Basic Starter Kit: https://www.amazon.co.uk/gp/product/B01LXTH7U1* Half-size solder less breadboard: https://www.amazon.co.uk/gp/product/B0739XRX8F* Temperature and Humidity Sensor Module DHT22/AM2302: https://www.amazon.co.uk/Digital-Temperature-Humidity-Electronic-Practice/dp/B091CP63LH* Breadboard jumper wires; male-to-male: https://www.amazon.co.uk/gp/product/B01LXTI2E3* Breadboard jumper wires; male-to-female: https://www.amazon.co.uk/AZDelivery-MB-102-Breadboard-Kit/dp/B07K8PVKBP* Battery holders for 3xAA and 4xAA: https://www.amazon.co.uk/gp/product/B08Z3RJC1Y* Camera module OV7670: https://www.amazon.co.uk/gp/product/B0919PWH4Q* Accelerometer sensor module MPU-6050: https://www.amazon.co.uk/gp/product/B07P5YXBXVSome of the links above are for larger quantity than strictly needed, so you might want to look for other listings or other retailers. Amazon review rules only allow me to include links within their site, but there are many other relevant retailers too such as thepihut, arduino store, adafruit etc.This is a great book to demystify the terminology of ML, get a feel for what it is really about, and try it out for yourself. Have fun!

Amazon Verified review

Samuel de Zoete Jan 17, 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 indept 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!

Amazon Verified review

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

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

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