O'Reilly、Cloudera 主办
Make Data Work
2017年7月12-13日:培训
2017年7月13-15日:会议
北京,中国

现实世界里的深度学习 (Deep learning in the real world)

This will be presented in English.

Lukas Biewald (CrowdFlower)
10:05–10:20 Saturday, 2017-07-15
英文讲话 (Presented in English)
地点: 紫金大厅A(Grand Hall A)

随着公司将机器学习从研发部门采用到生产系统中,他们面临着一系列新的挑战。 机器学习循环中的人、主动学习和迁移学习都是使深度学习能应用到现实世界的重要的设计模式。


As companies take machine learning out of R&D and into production, they face a whole new set of challenges. Lukas Biewald explains why human in the loop, active learning, and transfer learning are all essential design patterns for making deep learning real.

Photo of Lukas Biewald

Lukas Biewald

CrowdFlower

Lukas Biewald is the founder and chief data scientist of CrowdFlower, a data enrichment platform that taps into an on-demand workforce to help companies collect training data and do human-in-the-loop machine learning. Previously, he led the Search Relevance team for Yahoo Japan and worked as a senior data scientist at Powerset. Lukas was recognized by Inc. magazine as a 30 under 30. Lukas holds a BS in mathematics and an MS in computer science from Stanford University. He is also an expert Go player.

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