[PDF] Graph-Powered Machine Learning by

Graph-Powered Machine Learning.

Graph-Powered-Machine-Learning.pdf
ISBN: 9781617295645 | 496 pages | 13 Mb

• Graph-Powered Machine Learning
• Page: 496
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781617295645
• Publisher: Manning

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Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs

Graph-Powered Machine Learning: An Interview with Dr
explores the new way of applying graph-powered machine learning to recommendation engines, fraud detection systems, natural language processing.
Graph Powered Machine Learning: Part 1 - ML Summit
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other 
Graph-Powered Machine Learning - Digital Catalog
Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project. Graphs in big data platforms
Graph Powered Machine Learning in Smart Sensor Networks
The chapter covers the framework based on Graphical Features with some deep learning approaches such as GCN (Graph Convolutional Network) 
1 Machine learning and graphs: An introduction
Machine learning is a core branch of artificial intelligence: it is the field of study in computer science that allows computer programs to learn from data.
Introduction to Graph powered machine learning - Towards Dev
Graphs support machine learning by doing what they do best. Representing data in a way easily understandable and accessible. Graphs make all processes faster 

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