{pdf download} Graph-Powered Machine Learning

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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Free downloads of ebooks in pdf format Graph-Powered Machine Learning CHM (English Edition)

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

3 Graphs in Machine Learning Application
How to handle huge amount of data (big data) using a graph data model. This chapter will walk you through how to combine the power of the graph model as a way 
Graph-powered Machine Learning at Google
The Expander team's graph learning platform automatically generates graphs directly from data based on the inferred or known relationships 
Graph-Powered Machine Learning | GraphAware
Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms.
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 - Manning Publications
Data source modeling using graphs; Graph-based natural language processing, recommendations, and fraud detection techniques; Graph algorithms 
9781617295645: Graph-Powered Machine Learning
AbeBooks.com: Graph-Powered Machine Learning (9781617295645) by Nego, Alessandro and a great selection of similar New, Used and Collectible 
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
Graph Powered Machine Learning
Graph Embeddings and. Graph Neural Networks. Learning Graphs. -. Node/Link Classification. -. Link Prediction. -. Classification of Graphs.26 pages
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.
Graph-Powered Machine Learning First Steps - O'Reilly Media
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You'll learn why graphs 
Graph-powered Machine Learning - By Alessandro Nego
Read reviews and buy Graph-Powered Machine Learning - by Alessandro Nego (Paperback) at Target. Choose from Same Day Delivery, Drive Up or Order Pickup.Format: PaperbackGenre: Computers + Internet$42.99 · ‎In stock
Graph-Powered Machine Learning: Alessandro Nego
Graph-Powered Machine Learning by Alessandro Nego available in Trade Paperback on Powells.com, also read synopsis and reviews.
Graph-Powered Machine Learning Book - Skillsoft
This book teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms…
Graph-Powered Machine Learning - Manning Publications
The lifecycle of a machine learning project; Graphs in big data platforms; Data source modeling using graphs; Graph-based natural language 

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