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Author Warden, Pete, author

Title TinyML / Warden, Pete
Edition 1st edition
Published O'Reilly Media, Inc., 2019
Online access available from:
Safari O'Reilly books online    View Resource Record  


Description 1 online resource (350 pages)
Summary 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
Notes Mode of access: World Wide Web
Copyright © 2019 Pete Warden and Daniel Situnayake
Issuing Body Made available through: Safari, an O’Reilly Media Company
Notes Online resource; Title from title page (viewed December 25, 2019)
Form Electronic book
Author Situnayake, Daniel, author
Safari, an O’Reilly Media Company