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

Name: TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems , Second Edition embedded machine learning microcontroller projects real-world solutions
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SKU: 6036546378
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6036546378

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

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О книге Введение в книгу : Key benefits Over 20+ new recipes, including recognizing music genres and detecting objects in a scene Create practical examples using TensorFlow Lite for Microcontrollers, Edge ImpЧитать далее Описание Введение в книгу : Key benefits Over 20+ new recipes, including recognizing music genres and detecting objects in a scene Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device Description Discover 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!