Learning with limited labeled data
Who is this presentation for?
- Data scientists, machine learning engineers, and product managers
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.
Shioulin Sam explores algorithmic approaches that drive this capability and provides practical guidance for translating this capability into production. You’ll view a live demonstration to understand how and why these algorithms work.
- A basic understanding of math, classifiers, and neural networks
What you'll learn
- Learn classical active learning strategies (engineered heuristics) to choose the "best" data to label, active learning algorithms tailored for deep learning, an under-the-hood understanding of active learning, and when to use active learning and what to look out for
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. Previously, she managed a portfolio of early stage ventures focusing on women-led startups and public market investments and worked in the investment management industry designing quantitative trading strategies. She holds a PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.
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