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Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition Aldo Marzullo, Enrico Deusebio, Claudio Stamile - okladka książki

Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition Aldo Marzullo, Enrico Deusebio, Claudio Stamile - okladka książki

Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition Aldo Marzullo, Enrico Deusebio, Claudio Stamile - audiobook MP3

Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition Aldo Marzullo, Enrico Deusebio, Claudio Stamile - audiobook CD

Ocena:
Stron:
18
Graph Machine Learning, Second Edition not only revises but expands on its successful first edition, providing you with the latest tools and techniques in graph machine learning. This edition introduces comprehensive updates across all chapters, new chapters on trending topics like LLMs and Temporal Graph Learning, and real-world case studies that illustrate the practical applications of these concepts.
From basic graph theory to advanced machine learning models, the book guides you through understanding how data can be represented as graphs to uncover complex patterns and relationships hidden in your data. This edition emphasizes practical application with updated code examples using Pytorch Geometric, making it easier for you to implement what you learn.
The expanded content includes detailed chapters on using graph machine learning for dynamic and evolving data and integrating graph theory with Large Language Models (LLMs) for enriched data interaction and analysis. By the end of this book, you’ll not only be versed in the theory of graph machine learning but also adept at applying it to solve real challenges in innovative ways.

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O autorach książki

Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a thesis entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.
Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees in aerospace engineering from Politecnico di Torino.
Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Université Claude Bernard Lyon 1 (Lyon, France). During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.

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