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

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

The terms graph embeddings and graph representation learning can be seen as synonyms for 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).

Sachin will put more light on this rapidly growing area and its industrial and research applications in this session. 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.

Sachin Sharma ArangoDB

Sachin Sharma

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 the area of 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 conducted research in the areas of Computer Vision, NLP, and Graph Neural Networks at DFKI (German Research Centre for AI) during his academic career. Sachin also worked on building Machine Learning pipelines at Define Media Gmbh where he worked as a Machine Learning Engineer and Scientist.

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