deusebio enrico (22 resultados)

Condición: 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.

Condición: very_good. Very good condition. Light wear. May not include CD DVD, access code or any other supplemental materials.

paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

paperback. Condición: Very Good. Softcover shows only light cover wear. Text is unmarked and binding tight. Ships FAST!

Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condición: New.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New. This revised edition of Graph Machine Learning extends its coverage with new chapters on LLMs and temporal graph learning and updated libraries making it an essential resource for modern data scientists.

Paperback. Condición: New. Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relationship between nodes in order to make better business decisionsApply graph-based machine learning methods to solve real-life problemsBook DescriptionGraph 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 learnWrite Python scripts to extract features from graphsDistinguish between the main graph representation learning techniquesLearn how to extract data from social networks, financial transaction systems, for text analysis, and moreImplement the main unsupervised and supervised graph embedding techniquesGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and moreDeploy and scale out your application seamlesslyWho this book is forThis 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.

Paperback. Condición: New. This revised edition of Graph Machine Learning extends its coverage with new chapters on LLMs and temporal graph learning and updated libraries making it an essential resource for modern data scientists.

Condición: New. In.

Paperback. Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. This revised edition of Graph Machine Learning extends its coverage with new chapters on LLMs and temporal graph learning and updated libraries making it an essential resource for modern data scientists.

paperback. Condición: New. New. book.

Condición: New. Data scientists working with network data will be able to put their knowledge to work with this practical guide to building machine learning algorithms using graph data. The book provides a hands-on approach to implementation and associated methodologies th.

Paperback. Condición: New. Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relationship between nodes in order to make better business decisionsApply graph-based machine learning methods to solve real-life problemsBook DescriptionGraph 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 learnWrite Python scripts to extract features from graphsDistinguish between the main graph representation learning techniquesLearn how to extract data from social networks, financial transaction systems, for text analysis, and moreImplement the main unsupervised and supervised graph embedding techniquesGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and moreDeploy and scale out your application seamlesslyWho this book is forThis 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.

Paperback. Condición: New. This revised edition of Graph Machine Learning extends its coverage with new chapters on LLMs and temporal graph learning and updated libraries making it an essential resource for modern data scientists.