000 01577 a2200193 4500
020 _a9789355420435
082 _a006.31 TOK
100 _a Tok, Wee Hyong
245 _aPractical Weak Supervision : Doing More with Less Data
260 _bShroff/O'Reilly
_aUSA
_c2022
300 _a169
520 _aMost data scientists and engineers today rely on quality labeled data to train their machine learning models. But building training sets manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies pursue ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get a practical overview of weak supervision Dive into data programming with help from Snorkel Perform text classification using Snorkel's weakly labeled dataset Use Snorkel's labeled indoor-outdoor dataset for computer vision tasks Scale up weak supervision using scaling strategies and underlying technologies
650 _aSupervised learning (Machine learning)
650 _aNatural language processing (Computer science)
650 _aComputer vision
700 _aBahree, Amit
700 _aFilipi, Senja
942 _cBK
_2ddc
999 _c45809
_d45809