Machine Learning Algorithms

Lauren Shin, Neo4j Developer Relations Intern, introduces machine learning and offers three approaches to better analyze ML data.

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#GraphCast: Responsible AI

This week’s #GraphCast features Responsible AI where Amy Hodler discusses ethical development and application of artificial intelligence and technology.

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GraphConnect 2020 Agenda: Everything You Need to Know

The health and safety of our community, partners and employees are of the utmost importance to Neo4j. After monitoring the COVID-19 situation closely, we have decided to postpone GraphConnect 2020 in New York City to 2021. We are actively scoping… Read more →

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Ontologies in Neo4j: Semantics and Knowledge Graphs

Jesús Barrasa presents the two biggest uses of ontologies in graphs using Neo4j and NeoSemantics; inferences and interoperability.

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Connecting Healthcare

Learn about connecting healthcare data, including its challenges and how graph databases might be able to help solve healthcare’s wicked problems.

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Graphs to Fight Diabetes

Discover how Neo4j graph databases contribute to the study, understanding, prevention and treatment concerning diabetes with Alexander Jarasch from DZD.

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The Top 7 Neo4j Videos of 2019

In lieu of our bi-weekly #GraphCast, we thought we would share some of the best Neo4j videos from 2019. Consider this an almost-New Year’s Eve celebration of all things video. (No order or favorites here. Just cool videos in a… Read more →

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Graphs on GPUs: 5-Minute Interview with Saul Rosales

In this week’s five-minute interview (conducted at GraphTour DC 2019), we discuss how Saul Rosales and his team use Neo4j with Graphistry.

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Fullstack GraphQL with Neo4j

Dive into this introduction to GraphQL with Will Lyon. Built by Facebook, GraphQL is a query language to describe data sets and deliver predictable results.

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Real Examples of Why We Need Context for Responsible AI

Read part one of this two-part series on responsible AI. Discover real-world examples of the myths,., biases and more that currently surround AI.

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Will context fuel the next AI revolution?

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How Boston Scientific Improves Manufacturing Quality Using Graph Analytics

Editor’s Note: This presentation was given by Eric Wespi and Eric Spiegelberg at GraphConnect New York in September 2018. Presentation Summary We’re going to talk about some project themes that make sense when you’re going about developing a graph project.… Read more →

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Neo4j, ML, AI And Connected Data

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How Graphs Enhance Artificial Intelligence

Explore the increasing impact of graph technology on artificial intelligence and the steps toward enhancing AI and ML with a graph database.

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DeepWalk: Implementing Graph Embeddings in Neo4j

Discover tips and strategies for implementing graph embedding into a Neo4j graph database, with plenty of Cypher examples.

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Accelerating Towards Natural Language Search with Graphs

Learn how graphs are used for natural language processing, including loading text data, processing it for NLP, running NLP pipelines and building a knowledge graph.

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AI & Graph Technology: What Are Knowledge Graphs?

Read the second installment of this blog series on artificial intelligence on the ways knowledge graphs add context for decision support.

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AI and Graph Technology: 4 Ways Graphs Add Context

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

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Deciphering Product DNA: Next-Level PDM with AI & Knowledge Graphs

Increasingly complex products undoubtedly require greater management of components, function and data. Classic product data management (PDM) has long reach its limits in this respect. Breaking down product DNA is now driven by artificial intelligence (AI) and knowledge graphs. In… Read more →

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Toward AI Standards: Graph Technology for Responsible AI

Read the first installment of this four-part series on how graphs provide the context artificial intellingence needs to remain reliable and trustworthy.

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