Content Comparison

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Guest Speakers and subjects: 

1. Itauma Itauma, PhD Candidate, Keiser University
   

   TBC

   
   

2. Lili Xu, Software Engineer III, LexisNexis Risk Solutions
   TextVectors - Machine Learning for Textual Data
   Text Vectorization allows for the mathematical treatment of textual information.  Words, phrases, sentences, and paragraphs can be organized as points in high-dimensional space such that closeness in space implies closeness of meaning.  HPCC Systems' new TextVectors module supports vectorization for words, phrases, or sentences in a parallelized, high-performance, and user-friendly package.
   Roger is a Senior Architect responsible for the HPCC Systems Machine Learning Library.  He has been at HPCC Systems for nearly  three years.  He was previously at  CA Technologies.  Roger has been involved in the implementation and utilization of machine learning and AI techniques for many years, and has over 20 patents in diverse areas of software technologyAutomatically cluster your data with the HPCC Systems massively scalable K-Means machine learning bundle 
   
   Imagine you are sitting in front of thousands of articles and trying to organize them into different folders. How would you accomplish it and how long would you expect to finish it? Reading all the articles one by one and spending days or even months to finish the task? If you have some sort of data but have no clue how to efficiently cluster them, then this article should be a right place to start.
   

    

3. Bob Foreman, Senior Software Engineer, HPCC Systems, LexisNexis Risk Solutions
   ECL Tips and Tricks: TBC 

   
   

   Bob Foreman has worked with the HPCC Systems technology platform and the ECL programming language for over 5 years and has been a technical trainer for over 25 years. He is the developer and designer of the HPCC Systems Online Training Courses, and is the Senior Instructor for all classroom and Webex/Lync based training.