Hello Makers!
Join us as we discuss machine learning & data munging in H2O Driveless AI with datatable. Following is a brief agenda for the evening:
6:00 - 6:30 PM: Doors open for networking & pizza
6:30 - 7:30 PM: Presentation
7:30 - 8:00 PM: Q&A
8:00 PM - Event Ends
Description:
H2O datatable is a Python package for manipulating 2-dimensional tabular data structures, aka data frames. It is close in spirit to pandas, however, we put specific emphasis on speed & big data support. As the name suggests, the package is closely related to R's data.table & attempts to mimic its core algorithms & API. H2O datatable started in 2017 as a toolkit for performing big data operations on a single-node machine, at the maximum speed possible. Such requirements are dictated by modern machine-learning applications, such as H2O Driverless AI, which need to process large volumes of data & generate many features in order to achieve the best model accuracy. In the talk we will introduce H2O datatable, focusing on its data munging & modeling capabilities, followed by the Q/A session.
Speaker Bios:
Pasha Stetsenko: Pasha is a Hacker Scientist at H2O.ai. He holds an MS in Applied Physics & Mathematics from Moscow Institute of Physics & Technology, an MA in Economics from New Economic School (Moscow), & a PhD in Economics (econometrics) from Stanford University. During his education he obtained knowledge in Computer Science, Machine Learning, Statistics & Econometrics. Prior to coming to H2O.ai, Pasha was working at a stealth-level machine learning startup Machinify.com as a data scientist / frontend engineer; before that as an engineer at Facebook; & before as a senior quantitative analyst at a business consulting company Keystone Strategy, working on big data analysis.
Oleksiy Kononenko: Oleksiy is a maker scientist & hacker at H2O.ai, focusing on highly optimized algorithms for machine learning & data analysis. He holds M.S., summa cum laude, & Ph.D. degrees in applied mathematics from National University of Kharkiv, Ukraine. In 2009, Oleksiy was selected as a research fellow by CERN & contributed to R&D for Large Hadron Collider & next generation of high energy particle accelerators. In 2013 he joined SLAC & Stanford University to develop high performance simulation suite for 3D multi-physics modeling. Oleksiy authored more than 60 scientific papers, was an invited speaker at major international conferences, prominent institutions & companies worldwide. In his free time he enjoys snowboarding, playing soccer & basketball, guitar & drums, What? Where? When? & Jeopardy!
