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

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.

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!

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!

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!

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