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Edité par Packt Publishing (edition ), 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : BooksRun, Philadelphia, PA, Etats-Unis
EUR 25,61
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Ajouter au panierPaperback. Etat : Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Ajouter au panierpaperback. Etat : Very Good. Softcover shows only light cover wear. Text is unmarked and binding tight. Ships FAST!
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : California Books, Miami, FL, Etats-Unis
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Ajouter au panierEtat : New.
Edité par Packt Publishing 6/25/2021, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
EUR 50,49
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Ajouter au panierPaperback or Softback. Etat : New. Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms 1.28. Book.
Edité par Packt Publishing 2021-06-25, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierPaperback. Etat : New.
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Ajouter au panierEtat : As New. Unread book in perfect condition.
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Dream Books Co., Denver, CO, Etats-Unis
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Ajouter au panierEtat : acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 45,05
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierpaperback. Etat : New. New. book.
Edité par Packt Publishing Limited, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 54,61
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Ajouter au panierPAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par Packt Publishing Limited, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 59,22
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 74,89
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features:Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description: Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What You Will Learn:Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for: This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.