Knowledge Graphs, Sequence Translation and Machine Learning on Code

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This event is co-hosted with our friends at Metis SF Data Science (https://www.meetup.com/Metis-San-Francisco-Data-Science) Metis (https://thisismetis.com/), and source{d} (https://sourced.tech).

TALK 1: Answering English Questions using Knowledge Graphs and Sequence Translation

Abstract:
We’re going to create a system that is able to take an English language question, convert it into Cypher using a neural network, then run that query against a Neo4j graph database to produce an answer

Bio:
David Mack, Founder and Head of Research at Octavian.ai. Octavian is an early stage research organization focusing on machine learning on graphs. MSci Mathematics and the Foundations of Computer Science from the University of Oxford, BA Computer Science from the University of Cambridge.

TALK 2: Machine Learning on Source Code

Abstract:
source{d} is building the open-source components to enable large-scale code analysis and machine learning on source code. Their powerful tools can ingest all of the world’s public git repositories turning code into ASTs ready for machine learning and other analyses, all exposed through a flexible and friendly API. Francesc will show you how to run machine learning on source code with a series of live demos.

Bio:
Francesc Campoy is a Gopher, Host of the @justforfunc podcast, and VP of Developer relations at source{d}.