Presented by O'Reilly and Cloudera
Make Data Work
July 12-13, 2017: Training
July 13-15, 2017: Tutorials & Conference
Beijing, China
Lukas Biewald

Lukas Biewald
Founder and Chief Data Scientist , Weights & Biases

@l2k

Lukas Biewald is the founder and chief data scientist of Weights & Biases, 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.

议题

10:05–10:20 Saturday, 2017-07-15
英文讲话 (Presented in English)
Location: 紫金大厅A(Grand Hall A)
Lukas Biewald (Weights & Biases)
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. Read more.
11:15–11:55 Saturday, 2017-07-15
英文讲话 (Presented in English)
Location: 报告厅(Auditorium) 观众水平 (Level): Intermediate
Lukas Biewald (Weights & Biases)
平均得分:: ***..
(3.00, 2 次得分)
Training data collection strategies are often the most important and overlooked part of deploying real-world machine learning algorithms. Lukas Biewald explains why active learning is the best way to collect training data and can make the difference between a failed research project and a deployed production algorithm. Read more.
14:00–14:40 Saturday, 2017-07-15
英文讲话 (Presented in English)
Location: O’Reilly展位A桌 (Table A in O'Reilly Booth)
Lukas Biewald (Weights & Biases)
Best practices in training data collection and human-in-the-loop computing to make it possible to deploy imperfect machine learning algorithms for mission critical application. Lukas Biewald explains how you can make the best possible use of training data and why it is essential to making your machine learning work well. Read more.

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