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H2O.ai

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  • How to Approach a Multi-Class (classification) Problem
    How to Approach a Multi-Class (classification) Problem Ashrith Barthur, Chief Security Scientist at H2O.ai Recorded: Feb 5 2018 65 mins
    Ashrith's talk will show how to approach a multi-class (classification) problem using H2O. The data that is being used is an aggregated log of multiple systems that are constantly providing information about their status, connections and traffic. In large organizations, these log datasets can be very huge and unidentifiable due to the number of sources, legacy systems etc. In our example, we use a created response for each source. The use H2O to classify the source of data.

    Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behaviour in user activities, network traffic, attacks, financial fraud and global money movement. He has a PhD from Purdue University in the field of information security, specialized in Anomalous behaviour in DNS protocol.
  • Driverless AI - Your Expert System for AI
    Driverless AI - Your Expert System for AI Arno Candel, Chief Technology Officer at H2O.ai Recorded: Feb 1 2018 51 mins
    H2O.ai’s groundbreaking product, Driverless AI, is the intelligence of a Kaggle Grandmaster in a box. Driverless AI reduces processing time from days to mere hours and produces accurate and easily interpretable models. AI TO DO AI.

    Take a seat while Arno Candel discusses the benefits of Driverless AI and demonstrates:

    - 40x speedups using GPU enablement
    - Automatic feature engineering to build accurate prediction models
    - Model interpretability and technical reason codes in plain English

    Bio: Arno Candel is the Chief Technology Officer of H2O. He is also the main author of H2O’s Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.

    Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine.

    Follow him on Twitter: @ArnoCandel

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