Heena Chouhan Feb 07, 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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David L. Parnell Jul 01, 2024

My daughter loved it as she was working on some TinyML projects. Really a lot of hands on practical applied information of embedded ML applications.

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Vedat Ozan Oner Dec 10, 2023

I want to congratulate the author for his meticulous work in explaining the immense world of tinyML in the most understandable way! Before delving into the chapter specifics, allow me to highlight what I liked most about the book.- First and foremost, it explains how to utilize tinyML technology in real-world scenarios incredibly well, offering many recipes and balanced theory. The selection of problem domains in the book is perfect, clearly illustrating the various categories of problems that can be solved using ML techniques. Writing about ML requires a good amount of experience in the subject, and we sense the author's expertise right from the beginning.- It is much more than a tinyML book. Starting from the first chapter, you learn the basics of embedded development too. If you have some background in programming, you can take this book and step into the embedded world with tinyML! As a seasoned IoT developer, I can confidently attest to that.- Great selection of the software stack: the development environment is Arduino, making the learning process much smoother. Python is the programming language for designing and training the ML models in Google Colab. We also use Python on our development machines to develop some utility apps. Therefore, the software setup for the recipes of the book allows us to use our existing machines in the examples without any specific configuration, whether it is Linux, Windows, or Mac.- Great selection of hardware: We need only an Arduino Nano 33 BLE Sense, a Raspberry Pi Pico, and optionally a SparkFun Redboard Artemis Nano as the development boards. Other hardware components to run the examples are all quite common and widely available on the market with reasonable price tags. If you're an embedded developer, it is highly likely that you already have many of them in your drawer.- I liked the utilization of figures very much! If the current topic requires some theory to be discussed earlier, we can see a figure for it that visualizes the heart of the subject right in place. If we need a tool at a step, say a task from the Arduino IDE menu, the screenshot enables us to find it without doubt. For beginners in electronics, it is not an issue to set up the hardware in the examples with the figures that show each step on the breadboard.With that, let's see what we learn in each chapter:- Chapter 1, "Getting Ready to Unlock ML on Microcontrollers", prepares us for the recipes of the book by discussing the basics of machine learning, neural networks, tinyML, and electronics. We are introduced to the hardware and software platforms.- Chapter 2, "Unleashing Your Creativity with Microcontrollers", is the place where we learn embedded application development on the development kits. If you're experienced with those platforms and embedded development, you can skim this chapter and move on to the next one.- In Chapter 3, "Building a Weather Station with TensorFlow Lite for Microcontrollers", we start to learn TensorFlow Lite for Microcontrollers (TFLu), one of the frameworks that enable us to develop tinyML applications. The chapter shows us all the steps necessary to develop a full-fledged tinyML application.- In Chapter 4, "Using Edge Impulse and Arduino Nano to Control LEDs with Voice Commands", we see how to develop a keyword-spotting (KWS) application by employing Edge Impulse, a prominent online platform for tinyML development specifically. Audio analysis is one of the problem domains where we can successfully apply ML techniques, and the chapter teaches us how to prepare the model on Edge Impulse, then deploy it on the Arduino Nano for live inference.- Another audio classification example is given in two parts in Chapter 5 and Chapter 6, "Recognizing Music Genres with TensorFlow and Raspberry Pi Pico." We learn more ML techniques to classify music genres by using a Pico. It starts with the GTZAN dataset, the most popular public dataset for music genre recognition, and augments it with the audio data sampled by using Pico before training the model.- Chapter 7, "Detecting Objects with Edge Impulse using FOMO on Raspberry Pi Pico", talks about another problem domain where we can apply ML techniques: image classification. In the chapter, we learn how to have a head start with transfer learning in training our own model for image classification on the Edge Impulse platform.- In Chapter 8, "Classifying Desk Objects with TensorFlow and the Arduino Nano", we continue to practice image classification. This time, we attach an OV7670 camera module to Arduino Nano to take pictures of objects and classify them by using another tinyML model that we train on Colab. In the example, we import MobileNet v2 from Keras and train the tinyML model on top of it by applying transfer learning.- Chapter 9, "Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and Raspberry Pi Pico", opens a new page in the tinyML learning journey. We can develop gesture recognition applications by employing tinyML techniques, and that is exactly what we learn in this chapter with an example. We connect an MPU-6050 Inertial Measurement Unit (IMU) module to Pico and train a model on Edge Impulse with the data coming from the IMU module.- The interesting thing about Chapter 10, "Deploying a CIFAR-10 Model for Memory-Constrained Devices with Zephyr OS on QEMU", is that we see how to run a tinyML application on virtual hardware. After training an image classification model, we run it on an emulated Arm Cortex-M3 microcontroller.- Chapter 11, "Running ML models on Arduino and Arm Ethos-U55 microNPU using Apache TVM", introduces us to another tinyML framework - Apache TVM.- The final chapter, Chapter 12, "Enabling Compelling tinyML Solutions with On-device Learning and Scikit-Learn on the Arduino Nano and Raspberry Pi Pico", discusses the possibilities of training ML models on microcontrollers, another interesting topic for tinyML solutions.I highly recommend this book to discover the tinyML world in an easy and fast way, even if you're an absolute beginner in embedded development.

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Samuel de Zoete Jan 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!

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A. Fontaine Nov 30, 2023

Machine learning is not an easy topic to get into. There is just so much to know and so many tools, it’s hard to know where to start. TinyML Cookbook assumes no prior knowledge, either in machine learning or in embedded systems. This book is tutorial-based, giving you enough info and guidance so that you will be able to get a simple project working. By the time you get there you’ll have some good hands-on experience and several resources and tools to help you continue your journey.People at all levels of experience will find this book useful. I am an embedded developer with over 15 years of experience and two years ago I took an 8-month, extensive, in-depth course in machine learning. So far, I haven’t needed to use it in my career, but I assume it’s only a matter of time. Up until this book, I had never seen an ML model deployed on an embedded system, nor did I know how to bridge that gap.If you have no background in machine learning or Python, the topic may be overwhelming. There is a lot of work (feature engineering, model tuning) to get even a simple model to work. This book holds your hand the whole way, directing you to Google Colab notebooks and Arduino sketches which are available on GitHub for all of the exercises.This book will by no means make you an expert, but it will certainly get you oriented and get you on your way. It serves as an excellent launchpad. Beyond that, the journey is up to you.

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TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems , Second Edition

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