T�tulo: TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Editor: O'Reilly Media
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1492052043
ISBN 13: 9781492052043
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. As of early 2022, the supplemental code files are available at https: //oreil.ly/XuIQ4.

Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.

Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures
Work with Arduino and ultra-low-power microcontrollers
Learn the essentials of ML and how to train your own models
Train models to understand audio, image, and accelerometer data
Explore TensorFlow Lite for Microcontrollers, Google's toolkit for TinyML
Debug applications and provide safeguards for privacy and security
Optimize latency, energy usage, and model and binary size

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Pete Warden is technical lead for mobile and embedded TensorFlow. He was CTO and founder of Jetpac, which was acquired by Google in 2014, and previously worked at Apple. He was a founding member of the TensorFlow team, and blogs about practical deep learning at https: //petewarden.com.

Daniel Situnayake leads developer advocacy for TensorFlow Lite at Google. He co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.

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