Making the best possible use of training data is essential for effective machine learning. Active learning can make your training data collection 10x–1,000x more efficient, while transfer learning opens up a world of new training data possibilities. Lukas Biewald explores the state of the art in training data, active learning, and transfer learning, especially as applied to deep learning.
Lukas Biewald is the founder and chief data scientist of CrowdFlower, a data enrichment platform that taps into an on-demand to workforce to help companies collect training data and do human-in-the-loop machine learning. Previously, Lukas led the search relevance team for Yahoo Japan and was a senior data scientist at Powerset (acquired by Microsoft in 2008). He was featured on Inc. magazine’s 30 under 30 list. Lukas holds a BS in mathematics and an MS in computer science from Stanford. He is also an expert Go player.
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