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Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Mic

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Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN
1492052043
EAN
9781492052043
Binding
TP
Book Title
Tinyml: Machine Learning with Tensorflow Lite on A
Subject Area
Computers, Science
Publication Name
Tinyml : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Item Length
9.1 in
Publisher
O'reilly Media, Incorporated
Subject
Data Modeling & Design, General, Computer Vision & Pattern Recognition
Publication Year
2020
Type
Textbook
Format
Trade Paperback
Language
English
Item Height
1.1 in
Author
Daniel Situnayake, Pete Warden
Item Width
7 in
Item Weight
30 Oz
Number of Pages
501 Pages

About this product

Product Information

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

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1492052043
ISBN-13
9781492052043
eBay Product ID (ePID)
4038667237

Product Key Features

Author
Daniel Situnayake, Pete Warden
Publication Name
Tinyml : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Format
Trade Paperback
Language
English
Subject
Data Modeling & Design, General, Computer Vision & Pattern Recognition
Publication Year
2020
Type
Textbook
Subject Area
Computers, Science
Number of Pages
501 Pages

Dimensions

Item Length
9.1 in
Item Height
1.1 in
Item Width
7 in
Item Weight
30 Oz

Additional Product Features

LCCN
2020-277178
Lc Classification Number
Q325.5.W37 2020
Target Audience
Scholarly & Professional
Dewey Decimal
006.31
Dewey Edition
23
Illustrated
Yes

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