SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set ...

First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

Google’s machine learning-related algorithms. All the major search engines use machine learning in one or many ways. In fact, Microsoft is producing some significant breakthroughs.So are social ...

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science ...