000			02518cam a2200313 i 4500
003			SA-RiNAUS
005			20250904133551.0
008			210714s2020 cc a 001 0 eng d
020			_a9781492052043
082	0	4	_a006.31 _221 _bW P T
100	1		_aWarden, Pete, _eauthor. _945290
245	1	0	_aTinyML : _bmachine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers / _cPete Warden and Daniel Situnayake.
250			_aFirst edition.
264		1	_aBeijing _aBoston : _bO'Reilly, _c2020.
300			_axvi, 484 pages : _billustrations ; _c24 cm
500			_aIncludes index.
500			_aمقررات دراسية 2025-2026.
505	0		_aIntroduction -- 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.
520			_aDeep 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.
630	0	4	_aTensorFlow. _945291
650		0	_aMachine learning.
650		4	_aSignal processing _xDigital techniques. _945293
650		4	_aMicrocontrollers. _945294
700	1		_aSitunayake, Daniel, _eauthor _945296
942			_2ddc _cBK
999			_c35030 _d35029