Graph & Beyond Lunch Break #2.6: Graph Embeddings with AQL - ArangoDB

Graph & Beyond Lunch Break #2.6: Graph Embeddings with AQL

Graph & Beyond The Second Course #2.6: Graph Embeddings with AQL

The terms graph embeddings and graph representation learning can be seen as synonyms to each other where the key idea is to learn a mapping function that embeds nodes, or entire (sub)graphs (from non-euclidean), as points in low-dimensional vector space (to embedding space).

In this session, Sachin will put more light on this rapidly growing area and its industrial and research applications. In addition to this, he will also demonstrate how we can leverage graph embeddings with ArangoDB’s AQL query language. The key idea of this session would be to generate Amazon Product Recommendations with the help of ArangoDB’s  AQL query language.We are going to follow this github repository for our session.

About the Presenter:

Sachin is a Machine Learning Research Engineer at ArangoDB whose aim is to build Intelligent products using thorough research and engineering in Graph Machine Learning. He completed his Masters’s degree in Computer Science with a specialization in Intelligent Systems. He is an AI Enthusiast who has researched the areas of Computer Vision, NLP, and Graph Neural Networks at DFKI (German Research Centre for AI) during his academic career. Sachin also built Machine Learning pipelines at Define Media GmbH, where he worked as a Machine Learning Engineer and Scientist.

Register for lunch break:

Sachin Sharma ArangoDB

Sachin Sharma

Learn More About Graph Databases

Read our latest Graph and Beyond  white paper to gain insights into how ArangoDB graph databases can support many use cases.
DOWNLOAD NOW!
close-link
Click Me