Learning with Limited Labeled Data
Who is this presentation for?Data Scientists, Machine Learning Engineers, Product Managers
Prerequisite knowledgeBasic math, basic understanding of classifiers and neural networks
What you'll learn
Being able to teach machines with examples is a powerful capability, but it hinges on the availability of vast amounts of data. The data not only needs to exist, but has to be in a form that allows relationships between input features and output to be uncovered. Creating labels for each input feature fulfills this requirement, but is an expensive undertaking.
Classical approaches to this problem rely on human and machine collaboration. In these approaches, engineered heuristics are used to smartly select “best” instances of data to label, in order to reduce cost. A human steps in to provide the label; the model then learns from this smaller labeled dataset. Recent advancements have made these approaches amenable to deep learning, enabling models to be built with limited labeled data.
In this talk, we explore algorithmic approaches that drive this capability, and provide practical guidance for translating this capability into production. We provide intuition for how and why these algorithms work through a live demo.
Cloudera Fast Forward Labs
Shioulin Sam is a research engineer at Cloudera Fast Forward Labs, where she bridges academic research in machine learning with industrial applications. In her previous life, she managed a portfolio of early-stage ventures focusing on women-led startups and public market investments. She also worked in the investment management industry designing quantitative trading strategies. She holds a Ph.D in Electrical Engineering and Computer Science from Massachusetts Institute of Technology.
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