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Graph-Powered Machine Learning (Paperback)
Loot Price: R1,448
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Graph-Powered Machine Learning (Paperback)
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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 introduces you to graph technology
concepts, highlighting the role of graphs in machine learning and
big data platforms. You'll get an in-depth look at techniques
including data source modeling, algorithm design, link analysis,
classification, and clustering. As you master the core concepts,
you'll explore three end-to-end projects that illustrate
architectures, best design practices, optimization approaches, and
common pitfalls. Key Features * The lifecycle of a machine learning
project * Three end-to-end applications * Graphs in big data
platforms * Data source modeling * Natural language processing,
recommendations, and relevant search * Optimization methods Readers
comfortable with machine learning basics. About the technology By
organizing and analyzing your data as graphs, your applications
work more fluidly with graph-centric algorithms like nearest
neighbor or page rank where it's important to quickly identify and
exploit relevant relationships. Modern graph data stores, like
Neo4j or Amazon Neptune, are readily available tools that support
graph-powered machine learning. Alessandro Negro is a Chief
Scientist at GraphAware. With extensive experience in software
development, software architecture, and data management, he has
been a speaker at many conferences, such as Java One, Oracle Open
World, and Graph Connect. He holds a Ph.D. in Computer Science and
has authored several publications on graph-based machine learning.
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