000			01792nam a22002057a 4500
008			220823b2022 |||||||| |||| 00| 0 eng d
020			_a9789355420435
082			_a006.31 TOK-W
100			_aTok, Wee Hyong
245			_aPractical weak supervision : _bdoing more with less data / _cWee Hyong Tok, Amit Bahree and Senja Filipi
260			_aIndia _bShroff Publishers _c2022
300			_a169 p.
365			_aINR _b900.00.
500			_aMost data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi 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 have pursued 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 up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling.
650			_aSupervised learning (Machine learning)
650			_aNatural language processing (Computer science)
650			_aComputer vision
700			_aBahree, Amit
700			_aFilipi, Senja
999			_c80287 _d80287