£33.90

Springer Graph Representation Learning - AI and Machine Learning

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Description

Explore the fundamentals of graph-structured data with this expert text from Springer. As graph data becomes more common in fields like quantum chemistry and telecommunication networks, understanding how to build relational inductive biases into deep learning architectures is essential for modern research. This book covers the latest advancements in graph representation learning. You will find detailed information on deep graph embeddings and how to generalize convolutional neural networks for graph-structured data. It also examines neural message-passing approaches that draw inspiration from belief propagation. Designed for students and professionals in higher education, this volume provides the technical foundation needed to create systems that learn, reason, and generalize from complex relational data. It is a vital resource for anyone studying the intersection of artificial intelligence and machine learning.

Key Features

Covers deep graph embeddings and advanced techniques for representation learning.

Explains how to generalize convolutional neural networks for graph-structured data.

Discusses neural message-passing approaches inspired by belief propagation.

Provides insight into building relational inductive biases in deep learning.

Applicable to diverse fields including telecommunication and quantum chemistry.

Part of the Synthesis Lectures on Artificial Intelligence and Machine Learning series.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
16 September 2020
Listed Since
01 June 2022

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