The Locator -- [(subject = "Microcontrollers")]

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02896aam a2200397Ii 4500
001 241AF5385D1D11EA9B49BA2197128E48
003 SILO
005 20200303010150
008 190610t20202020caua          001 0 eng d
020    $a 9781492052043
020    $a 1492052043
035    $a (OCoLC)1104044619
040    $a YDX $b eng $c YDX $d BDX $d JRZ $d CLE $d OCLCF $d SILO
050  4 $a Q325.5 $b .W37 2020
082 04 $a 006.31 $2 23
100 1  $a Warden, Pete $e author.
245 10 $a TinyML : $b machine learning with TensorFlow Lite on Arduino and ultra-low power microcontrollers / $c Pete Warden and Daniel Situnayake.
250    $a First edition.
264  1 $a Sebastopol, CA : $b O'Reilly Media Inc., $c [2020]
300    $a xvi, 484 pages : $b illustrations ; $c 24 cm
500    $a Includes index.
520    $a 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.
505 0  $a Introduction -- Getting started -- Getting up to speed on machine learning -- The "Hello world" of TinyML : building and training a model -- The "Hello world" of TinyML : building an application -- The "Hello world" of TinyML : deploying to microcontrollers -- Wake-word detection : building an application -- Wake-word detection : training a model -- Person detection : building an application -- Person detection : training a model -- Magic wand : building an application -- Magic wand : training a model -- TensorFlow lite for microcontrollers -- Designing your own TinyML applications -- Optimizing latency -- Optimizing energy usage -- Optimizing model and binary size -- Debugging -- Porting models from TensorFlow to TensorFlow Lite -- Privacy, security, and deployment -- Learning more.
630 00 $a TensorFlow.
630 04 $a TinyML.
650  0 $a Machine learning.
650  0 $a Signal processing $x Digital techniques.
650  0 $a Microcontrollers.
650  7 $a Machine learning. $2 fast $0 (OCoLC)fst01004795
650  7 $a Microcontrollers. $2 fast $0 (OCoLC)fst01744800
650  7 $a Signal processing $x Digital techniques. $2 fast $0 (OCoLC)fst01118285
700 1  $a Situnayake, Daniel, $e author
941    $a 1
952    $l USUX851 $d 20220802021821.0
956    $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=241AF5385D1D11EA9B49BA2197128E48
994    $a 92 $b IWA

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