000			02165cam a22002777a 4500
001			TB12444
003			IN-BhIIT
005			20260310154943.0
008			210714s2020 cc a 001 0 eng d
020			_a9789352139606 (PBK)
040			_aIN-BhIIT
041			_aeng
082	0	4	_a006.31 _bWAR/T
100	1		_aWarden, Pete _eAuthor _926079
245	1	0	_aTinyML : _bmachine learning with tensorflow lite on arduino and ultra-low-power microcontrollers / _cPete Warden and Daniel Situnayake.
260			_aMumbai : _bO'Relly Media Inc., _c2021.
300			_axvi, 484 pages : _billustrations ; _c24 cm
500			_aIncludes bibliographical references and 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 becoming smaller, with models as small as 14 kilobytes. This practical book, TinyML, combines deep learning and embedded systems to create small, portable devices. It provides a step-by-step guide for developers to create TinyML projects, including speech recognition, camera detection, and gesture response. The book also covers learning ML basics, using TensorFlow Lite for microcontrollers, and optimizing latency and energy usage.
650		0	_aMachine learning. _927211
650		0	_aSignal processing _xDigital techniques. _92423
650		0	_aMicrocontrollers. _95513
700	1		_aSitunayake, Daniel _eJoint author _926080
942			_cTB _02
999			_c14965 _d14965