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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 IWAInitiate Another SILO Locator Search