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Thursday, March 23, 2017 at 06:00 PM    Cost: Absolutely Free
WeWork 300 Park, 300 Park Ave

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6:00-6:30 - Doors Open + Pizza

6:30-7:15 - Michal's Talk

7:15-8:00 - Megan's Talk

8:00 - Q&A

Abstract: The focus of this talk is to provide an introduction to Natural Language Processing. We will use a Word2Vec model to convert the movie synopses to a numeric vector known as word embeddings. From there, we can build a supervised learning model that will predict the movie genre based on these word embeddings. The talk will cover the theory behind Word2Vec as well as tips & tricks for building supervised learning models. We will use H2O, an open source, distributed machine learning platform, to implement these models.


Michal Kurka is a software engineer at He has a background in architecting big data platforms that utilize machine learning. In he works on core & algorithms, he is responsible for H2O's implementation of the word2vec algorithm. He holds a Master of Computer Science from Charles University in Prague. His field of study was Discrete Models & Algorithms with a focus on Optimization.


Megan is a Customer Data Scientist at H2O. Prior to working at H2O, she worked as a Data Scientist building products driven by machine learning for B2B customers. She has experience working with customers across multiple industries, identifying common problems, & designing robust & automated solutions.


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