02165cam a22002777a 4500 TB12444 IN-BhIIT 20260310154943.0 210714s2020 cc a 001 0 eng d 9789352139606 (PBK) IN-BhIIT eng 006.31 WAR/T Warden, Pete Author 26079 TinyML : machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers / Pete Warden and Daniel Situnayake. Mumbai : O'Relly Media Inc., 2021. xvi, 484 pages : illustrations ; 24 cm Includes bibliographical references and index. 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. Deep 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. Machine learning. 27211 Signal processing Digital techniques. 2423 Microcontrollers. 5513 Situnayake, Daniel Joint author 26080 TB 2 14965 14965 0 0 ddc 0 0 SMS CLIITBBS CLIITBBS 2025-03-06 22 1425.00 2 1 006.31 WAR/T TB12444 2026-05-13 13:14:53 2026-03-10 1900.00 2025-03-06 TB