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TinyML : Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers by Daniel Situnayake and Pete Warden (2020, Trade Paperback)
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Title: Tinyml: Machine Learning with Tensorflow on Arduino, and Ultra-L Item Condition: New. Author: Pete Warden, Daniel Situnayake ISBN 10: 1492052043. Binding: Paperback Language: english. Published On: 2019-12-31 SKU: 4444-GRD-9781492052043.
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101492052043
ISBN-139781492052043
eBay Product ID (ePID)4038667237
Product Key Features
Number of Pages350 Pages
Publication NameTinyml : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
LanguageEnglish
SubjectData Modeling & Design, General, Computer Vision & Pattern Recognition
Publication Year2020
TypeTextbook
AuthorDaniel Situnayake, Pete Warden
Subject AreaComputers, Science
FormatTrade Paperback
Dimensions
Item Height1 in
Item Weight29.6 Oz
Item Length9.1 in
Item Width7.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2020-277178
Dewey Edition23
IllustratedYes
Dewey Decimal006.31
SynopsisNeural 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, 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, 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
LC Classification NumberQ325.5.W37 2020
