Graph-based machine learning is a trend in Artificial Intelligence that is gaining popularity due to its many advantages. When using graphs as a basic representation of data for ML purposes, you can benefit from the data being explicitly modeled for further analysis, representing connections and relationships between things and concepts. Additionally, graphs can easily combine multiple sources into a single graph representation and learn from them, creating powerful knowledge graphs. These benefits can lead to improved computation performance and higher quality results.
During this talk, you’ll learn about the many benefits of using graph-based machine learning and how it can be applied in the context of recommendation engines and natural language processing. Don’t miss out on this opportunity to learn more about this exciting trend and how it can help you unlock the full potential of your data.
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Leverage intelligence-led policing with mission-critical graph analytics capabilities.
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Gain capabilities to act quickly, stop fraud and protect your clients and your business.
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Enable automation, learn about bad actors and their networks, and leverage predictive strategies.
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New use cases, features, and live demos designed to make analysts’ lives easier.
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Our research, philosophy and case studies, all wrapped up in books and papers.
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Discover GraphAware Hume’s features, demos, and graph technology insights.
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Review our in-depth user guides and technical documentation to ensure flawless operations.
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