Graph-Powered Machine Learning by Alessandro Nego on Audiobook New
Graph-Powered Machine Learning. Alessandro Nego
Graph-Powered-Machine-Learning.pdf
ISBN: 9781617295645 | 496 pages | 13 Mb
- Graph-Powered Machine Learning
- Alessandro Nego
- Page: 496
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781617295645
- Publisher: Manning
Free e books and journals download Graph-Powered Machine Learning 9781617295645 by Alessandro Nego (English literature) iBook
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 - Manning Publications
Data source modeling using graphs; Graph-based natural language processing, recommendations, and fraud detection techniques; Graph algorithms
Graph-Powered Machine Learning - Manning Publications
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered
Graph Powered Machine Learning for Financial Crime - A new
Applications of property graphs and machine learning on big data is bringing a new approach to financial crime and fundamentally changing
Graph-Powered Machine Learning (Paperback) - Lake Forest
Sep 28, 2021 — In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project. Graphs in big data platformsRelated Editions (all): Kobo eBook (October 4th, Publication Date: September 28th, 2021Publisher: Manning
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.
3 Graphs in machine learning applications - O'Reilly Media
learning workflow How to store the training data and the resulting model properly Graph-based … - Selection from Graph-Powered Machine Learning [Book]
Graph-Powered Machine Learning (Paperback) - Greenlight
Description. Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.
Graph Powered Machine Learning - Eventbrite
Eventbrite - ArangoDB presents Graph Powered Machine Learning - Thursday, February 17, 2022 at Salesforce Tower, San Francisco, CA.
Graph-Powered Machine Learning (Paperback) - Politics and
In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project. Graphs in big data platforms
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
Graph-Powered Machine Learning Essential Excerpts - Tech
gence” of the machine learning algorithms based on graph theory. Provide predictions. Figure 3.2 Mental model for graph-powered machine learning.
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