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Published on May 12, 2015
Graphs are a perfect solution to organize information and to determine the relatedness of content. In this webinar, Neo4j Developer Evangelist Kenny Bastani will discuss using Neo4j to perform document classification. He will demonstrate how to build a scalable architecture for classifying natural language text using a graph-based algorithm called Hierarchical Pattern Recognition. This approach encompasses a set of techniques familiar to Deep Learning practitioners. Kenny will then introduce a new Neo4j unmanaged extension that can train natural language models on Wikipedia articles to determine which articles are most related based on a vector of shared features.
Speaker: Kenny Bastani, Developer Evangelist, Neo Technology Kenny Bastani is an accomplished software development consultant and entrepreneur with 10+ years of industry experience as a front-end and back-end engineer. Kenny has demonstrated leadership in designing and developing enterprise-grade web applications for high-volume, high-availability environments, with innovative focuses on solving unsupervised machine learning problems that enable businesses to better manage their institutional memory. As both an entrepreneur and software designer based in the SF Bay Area, Kenny has gained valuable experience leading teams in both product design and software architecture.