Hands-On Graph Neural Networks Using Python
Hands-On Graph Neural Networks Using Python is a comprehensive guide to building and training graph neural networks for a variety of real-world applications. With clear explanations and plenty of hands-on examples, this book is a valuable resource for anyone looking to learn about and apply graph neural networks to their own projects.
Offered by
Difficulty Level
Intermediate
Completion Time
11h48m
Language
English
About Book
Who Is This Book For?
This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
Hands-On Graph Neural Networks Using Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h48m total length
Getting Started with Graph Learning
Graph Theory for Graph Neural Networks
Creating Node Representations with DeepWalk
Improving Embeddings with Biased Random Walks in Node2Vec
Including Node Features with Vanilla Neural Networks
Introducing Graph Convolutional Networks
Graph Attention Networks
Scaling Graph Neural Networks with GraphSAGE
Defining Expressiveness for Graph Classification
Predicting Links with Graph Neural Networks
Generating Graphs Using Graph Neural Networks
Learning from Heterogeneous Graphs
Temporal Graph Neural Networks
Explaining Graph Neural Networks
Forecasting Traffic Using A3T-GCN
Detecting Anomalies Using Heterogeneous Graph Neural Networks
Building a Recommender System Using LightGCN
Unlocking the Potential of Graph Neural Networks for Real-Word Applications
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