Practical Weak Supervision: Doing More With Less Data

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Practical Weak Supervision: Doing More With Less DataMost 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

The book also reveals the top 10 archetypes they use

including routines for the Hamming code More coverage of integer division constants

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it ends exploring very different topics

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