In questo webinar vi presenteremo come le potenzialità del machine learning basato sulla tecnologia di Neo4j orientata ai grafi permetta di realizzare previsioni più accurate. In precedenza accessibile solo ai ricercatori e a pochissime aziende tecnologiche avanzate, Neo4j ha democratizzato le tecniche ML basate sui grafi che sfruttano il deep learning e le reti neurali convoluzionali del grafico.
La maggior parte dei modelli di data scince ignora la struttura della rete mentre i grafi aggiungono funzionalità altamente predittive ai modelli di ML, aumentando la precisione e consentendo previsioni altrimenti irraggiungibili senza considerare le relazioni tra i dati. Con il recente aggiornamento della libreria Neo4j Graph Data Science (GDS), puoi trarre vantaggio da questi algoritmi per previsioni nuove e più accurate con i dati che già hai in possesso.
Unisciti a noi per questo webinar per nuovi suggerimenti e trucchi e ottimizzare i tuoi progetti.
Riccardo has extensive sales experience selling enterprise software solutions to Italian market for almost 25 years. Riccardo has sold into a variety of organizations in multiple industries (Finance, MFG, Utilities, Automotive, Aerospace & Defense, Machinery), created positive business relations, identified new sales opportunity and securing large deals.
He is joins Neo4j from IHS Markit, where he was in Oil & Gas and Aerospace & Defense industries, growing the Italian market for IHS. He also worked for PTC, Veritas/Symantec, Fast Search, Microsoft and Autodesk. l
Marco is responsible for pre-sales activities and first point-of-contact for customer success for the Italian market. Marco loves switching on light bulbs with people, facilitating those ‘aha moments’ that change mindsets and open up new possibilities. Before joining Neo4j, Marco was a Solution Design Manager at CAST where he worked on Software Intelligence, cybersecurity, and data protection. Author and co-author of articles and scientific publications in international conferences/journals, in his previous academic career as Ph.D. at Politecnico di Milano, focused his researches on green software and software energy optimization.
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