000			02600 a2200337 4500
003			OSt
005			20260128113102.0
008			251106b |||||||| |||| 00| 0 eng d
020			_a9789352139606 _q(paperback)
040			_beng _cSAIU _dSAIU _erda _aSAIU
082			_223 _a006.31
100	1		_aWarden, Pete, _eauthor
245	1	0	_aTinyML : _bmachine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers / _cPete Warden and Daniel Situnayake.
250			_aFirst edition. _b2nd Indian Reprint 2024
260			_aIndia : _bShroff / O'Reilly Media, _c2019
264	1		_aIndia : _bShroff / O'Reilly Media, _c2019
300			_axvi, 484 pages : _billustrations ; _c24 cm
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
500			_aIncludes index.
505			_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	0	_aTensorFlow
630	0	4	_aTinyML
650		0	_aMachine learning
650		0	_aSignal processing _xDigital techniques
650		0	_aMicrocontrollers
700	1		_aSitunayake, Daniel, _eauthor
942			_cBK _2ddc
999			_c6882 _d6882