000			02792nam a22003977a 4500
001			22132416
003			IIITD
005			20230907124830.0
008			210714s2020 cc a 001 0 eng d
010			_a 2020277178
020			_a9789352139606
035			_a(OCoLC)on1104044619
040			_aYDX _beng _cYDX _dBDX _dJRZ _dCLE _dOCLCF _dDLC
042			_alccopycat
050	0	0	_aQ325.5 _b.W37 2020
082	0	4	_a006.31 _223 _bWAR-T
100	1		_aWarden, Pete
245	1	0	_aTinyML : _bmachine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers _cby Pete Warden and Daniel Situnayake
260			_aNew Delhi : _bShroff Publishers, _c©2021
300			_axvi, 484 p. : _bill. ; _c24 cm
500			_aIncludes index.
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	0	_aTensorFlow.
630	0	4	_aTinyML.
650		0	_aMachine learning.
650		0	_aSignal processing _xDigital techniques.
650		0	_aMicrocontrollers.
650		7	_aMachine learning. _2fast
650		7	_aMicrocontrollers. _2fast
650		7	_aSignal processing _xDigital techniques. _2fast
700	1		_aSitunayake, Daniel
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c171340 _d171340