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Viser: TinyML - Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
TinyML Vital Source e-bog
Pete Warden og Daniel Situnayake
(2019)
TinyML
Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Pete Warden og Daniel Situnayake
(2020)
Sprog: Engelsk
om ca. 15 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: O'Reilly Media, Inc (December 2019)
- Forfattere: Pete Warden og Daniel Situnayake
- ISBN: 9781492051992
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Paperback: 350 sider
- Udgiver: O'Reilly Media, Incorporated (Januar 2020)
- Forfattere: Pete Warden og Daniel Situnayake
- ISBN: 9781492052043
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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