Last week, we organized our 2nd in person MLonCode Meetup in partnership with Neo4j and METIS.

David Mack from first gave a talk on how to get started with Machine Learning on graphs. He showed 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.

You can find David’s slides below and here is a link to the very detailed blog post he wrote on the same topic.

Next up, we had Francesc Campoy giving a talk on Machine Learning on Source Code, walking the audience through the following challenges.

Challenge #1: Data Retrieval

Challenge #2: Data Analysis

Challenge #3: Learning from source Code

Challenge #4: Apply Machine Learning to source code

Francesc showed the audience how source{d} Engine simplifies the process of retrieving code from various git repositories and turning it into language agnostic Abstract Syntax Trees called UASTs that can be analyzed through a flexible and friendly SQL API.

Francesc also introduce source{d} Lookout, a platform helping developers with assisted code review through source code analyzers.

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