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 H2O.ai. He has a background in architecting big data platforms that utilize machine learning. In H2O.ai 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.