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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers - Softcover
Synopsis
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
- Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
- Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
- Understand how to work with Arduino and ultralow-power microcontrollers
- Use techniques for optimizing latency, energy usage, and model and binary size
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About the Author
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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- PublisherO'Reilly Media
- Publication date2019
- ISBN 10 1492052043
- ISBN 13 9781492052043
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages504
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