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Data Science in 30 Minutes: Examining Machine Learning Trends with Cloudera...

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New York, NY 10003

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Join The Data Incubator and Shioulin Sam, Research Scientist at Cloudera Fast Forward Labs for the next installment of our free online webinar series, Data Science in 30 Minutes: Examining Machine Learning Trends

Abstract: We will explore the latest and greatest in machine learning, including (but not limited to) semantic recommendations and multi-task learning. In regard to semantic recommendations, we will discuss how multi-modal embeddings - an emerging technique from deep learning - enable us to build a better system that actually understands content. We will also look at how multi-task learning - an approach in which models are trained to learn related tasks in parallel - is central to the notion of Software 2.0, and helps computers learn more the way we do. We will showcase both capabilities with a live demo of our prototypes.

About the speakers:

Shioulin Sam

Shioulin Sam, is a research engineer at Cloudera Fast Forward Labs. In her previous life, she was an angel investor focusing on women-led start-ups. She also worked in the investment management industry designing quantitative trading strategies. She holds a Ph.D in Electrical Engineering and Computer Science from Massachusetts Institute of Technology.


Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners.

Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves.

Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.

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New York, NY 10003

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