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Graph-Powered Machine Learning Manning Book by Alessandro Negro, Chief Scientist at GraphAware

Graph-Powered Machine Learning - Book

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.

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How to Know What You Know: 5-Minute Interview with Dr. Alessandro Negro, Chief Scientist at GraphAware

How to Know What You Know: 5-Minute Interview with Dr. Alessandro Negro, Chief Scientist at GraphAware

Read the interview with our Chief Data Scientist Alessandro Negro published on Neo4j blog, where he talks about how GraphAware uses natural language processing to help companies gain a better understanding of the knowledge that is spread across their organization.

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Neo4j : Déploiement - how to use Neo4j in a real life project

Neo4j : Déploiement - how to use Neo4j in a real life project

GraphAware is pleased to announce the release of “Neo4j : Déploiement”, a french book explaining how to use Neo4j in a real life project. The book is co-authored by Sylvain Roussy and Nicolas Rouyer along with our Senior Consultant Nicolas Mervaillie. You can get it from your favorite (french) bookstore or on D-Booker website.

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GraphAware Towards Online Analytical Processing in Graph Databases

GraphAware: Towards Online Analytical Processing in Graph Databases

GraphAware: Towards Online Analytical Processing in Graph Databases

MSc. Thesis submitted for MSc. program in Computing at Imperial College London, written by GraphAware's managing director Michal Bachman.

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