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Neo4j, The Leader in Graph Databases
Graph Embeddings: The Secret Ingredient for Relationship-Driven AI
Maybe you’ve heard about using graph data, or relationships, in machine learning pipelines for better accuracy or new types of predictions. But you’ve probably wondered: how exactly do I improve my predictive accuracy? In this 60-minute webinar, you’ll learn about graph embeddings: a data encoding technique to make highly accurate predictions based on graph structure.
Dr. Alicia Frame will provide an overview of:
What graph embeddings are and the science behind them
What graph embeddings can be used for, from graph completion to predictive pipelines
How to select the right graph embedding for your data set and problem
How to use Neo4j’s Graph Data Science Library to generate graph embeddings from your data
Zach Blumenfeld will do a hands-on demo of graph embeddings from a consumer data set to make novel and insightful recommendations.
Director of Product Management, Graph Data Science, Neo4j
Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a phd in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.
She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.
Data Science Product Specialist, Neo4j
Zach Blumenfeld is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement Graph Analytics to solve challenging business problems.
He has first hand experience with a wide range of modern day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing for law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full stack graph systems designed to facilitate broad search and analytical query requirements.
Zach is excited to have recently joined Neo4j as Data Science Product Specialist, where he will help empower the field with Neo4j’s industry leading Graph Data Science (GDS) capabilities.
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