Using a Graph Database for Deep Learning Text Classification

Graphify is a Neo4j unmanaged extension that provides plug and play natural language text classification.

Graphify gives you a mechanism to train natural language parsing models that extract features of a text using deep learning. When training a model to recognize the meaning of a text, you can send an article of text with a provided set of labels that describe the nature of the text. Over time the natural language parsing model in Neo4j will grow to identify those features that optimally disambiguate a text to a set of classes.

Understanding How Neo4j Cypher Queries are Evaluated

There are many ways to store and manage data within a Neo4j graph database. When Neo4j 2.0 launched late last year we had an entirely new browser experience for interacting with graphs. The graph visualization from the return results of Cypher queries were at the core of the user experience enhancements to the platform.

Using 3D Visualization to Debug a Graph-based Algorithm

Recently I have been working on an idea for an algorithm that discovers patterns in raw streams of data. This pattern recognition algorithm uses deep learning to classify certain combinatorial features that uniquely identify an input stream.

I'm going to first talk a bit about the algorithm so it makes sense as to why visualization is such an important step in iterating and tweaking code that most efficiently implements the algorithm.

The Algorithm

In a previous post I introduce the idea for the algorithm and how a graph-based approach might work.