Bøger af Gian Marco Iodice

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 learningPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesOver 20+ new recipes, including recognizing music genres and detecting objects in a sceneCreate practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and moreExplore cutting-edge technologies, such as on-device training for updating models without data leaving the deviceBook DescriptionDiscover 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 learnUnderstand the microcontroller programming fundamentalsWork with real-world sensors, such as the microphone, camera, and accelerometerImplement an app that responds to human voice or recognizes music genresLeverage transfer learning with FOMO and KerasLearn best practices on how to use the CMSIS-DSP libraryCreate a gesture-recognition app to build a remote controlDesign a CIFAR-10 model for memory-constrained microcontrollersTrain a neural network on microcontrollersWho this book is forThis 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 ContentsGetting Ready to Unlock ML on MicrocontrollersUnleashing Your Creativity with MicrocontrollersBuilding a Weather Station with TensorFlow Lite for MicrocontrollersUsing Edge Impulse and the Arduino Nano to Control LEDs with Voice CommandsRecognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi PicoClassifying Desk Objects with TensorFlow and the Arduino NanoBuilding 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)

Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learningKey FeaturesTrain and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi PicoWork with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge ImpulseExplore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPUBook DescriptionThis 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.What you will learnUnderstand the relevant microcontroller programming fundamentalsWork with real-world sensors such as the microphone, camera, and accelerometerRun on-device machine learning with TensorFlow Lite for MicrocontrollersImplement an app that responds to human voice with Edge ImpulseLeverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE SenseCreate a gesture-recognition app with Raspberry Pi PicoDesign a CIFAR-10 model for memory-constrained microcontrollersRun an image classifier on a virtual Arm Ethos-U55 microNPU with microTVMWho this book is forThis 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.Table of ContentsGetting Started with TinyMLPrototyping with MicrocontrollersBuilding a Weather Station with TensorFlow Lite for MicrocontrollersVoice Controlling LEDs with Edge ImpulseIndoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino NanoBuilding a Gesture-Based Interface for YouTube PlaybackRunning a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OSToward the Next TinyML Generation with microNPU