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Topic modeling is a statistical technique that allows the discovery of hidden thematic structures or “topics” in text. Topic modeling algorithms allow us to summarize, search, and explore large collections of documents in new ways. This talk will introduce Topic Modeling and go through the different types of algorithms and implementations available. The talk will give an overview of gensim, a Python library for Topic Modeling, and describe the process of data cleaning, model training, and results interpretation.
Mohamed Amin is a Masters candidate in the Technology Innovation Management program at Carleton University with a Software Engineering background. His current research focuses on business models and differentiation in the API Economy. He worked in the Telecom industry before joining Carleton University and did consulting work for local startups in Ottawa.
Have you ever wanted lightning-fast code, without the segfaults and tedious boilerplate of C? Then come learn why so many Pythonistas are excited about Rust, a new language from Mozilla. After a short introduction to the language, we'll see how the Rust Python bindings let us easily write elegant, safe, and high-performance extension modules, hassle-free.
Samuel Cormier-Iijima is a senior engineer at SurveyMonkey. He previously co-founded Fluidware, which grew to 80 employees before being acquired in 2014. He is passionate about the web, cryptography, computer science and mathematics, and contributes to a number of Python and Rust open source projects.