Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255

This article discusses active learning as a method for building applications that require a small amount of labeled data. It features an interview with Shioulin Sam, a Research Engineer at Cloudera Fast Forward Labs, focusing on their recent report, "Learning with Limited Label Data." The conversation likely covers the principles of active learning and its growing relevance in deep learning applications. The article's focus suggests an exploration of techniques to improve model training efficiency when labeled data is scarce, a common challenge in many AI projects. The interview format indicates a practical, accessible approach to explaining the topic.

• •Active learning is a method for training models with limited labeled data.
• •The article discusses a report from Cloudera Fast Forward Labs on this topic.
• •The interview format suggests a practical discussion of the technology.