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Tinyml : Machine Learning avec Tensorflow Lite sur Arduino et Ultra-Low-Power

33,23 $US
Environ45,11 $C
État :
Bon
Expédition :
Sans frais Standard Shipping.
Lieu : Sparks, Nevada, États-Unis
Livraison :
Livraison prévue entre le jeu. 26 sept. et le mar. 1 oct. à 43230
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Caractéristiques de l'objet

État
Bon: Un livre qui a été lu, mais qui est en bon état. La couverture présente des dommages infimes, ...
Book Title
Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultr
Publication Date
2020-01-21
Pages
501
ISBN
9781492052043
Subject Area
Computers, Science
Publication Name
Tinyml : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Publisher
O'reilly Media, Incorporated
Item Length
9.1 in
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 Weight
30 Oz
Item Width
7 in
Number of Pages
501 Pages

À propos de ce produit

Product Identifiers

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

Product Key Features

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

Dimensions

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

Additional Product Features

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

Description de l'objet du vendeur

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Moyenne au cours des 12 derniers mois
Qualité de la description
4.9
Justesse des frais d'expédition
4.9
Rapidité de l'expédition
4.9
Communication
4.9

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  • p***p (47)- Évaluation laissée par l'acheteur.
    Six derniers mois
    Achat vérifié
    Great seller! Item is what I ordered; good communication; shipped promptly; good value. NOTE TO SELLER: packaging was NOT appropriate for item; it was a flimsy, plastic envelope, with no stiff material to prevent creases. The book came with two deep creases that involved the *entire* item: one is a 1" triangle lower left side (bound edge); the other is a 7" triangle on upper right side (open edge).
  • s***s (126)- Évaluation laissée par l'acheteur.
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    This is an outstanding seller to deal with. Fair prices that are more than reasonable in this economy. The product is in better condition than described, a true value for my money. Packaged and shipped well shows seller has concern for the products he sells to arrive in excellent condition. The seller is friendly and communicates timely with his customers. I highly recommend this seller and would do business again anytime. Thank you!
  • o***o (80)- Évaluation laissée par l'acheteur.
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    The seller charged a very reasonable price and shipped it quickly in a sturdy package. It arrived fast and in perfect condition. There was no need for further communication. They did a great job and I strongly recommend this seller.

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