Hands-On Graph Neural Networks Using Python
Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Created by Maxime Labonne
Explore how to build and train graph neural networks using Python and PyTorch Geometric. Gain practical experience working with graph data and learn to apply these models to real-world problems in areas like recommendation systems and traffic forecasting. Develop skills that help you create, analyze, and deploy powerful graph-based solutions.
Packt | Apr 2023 | 354 min
What You Will Learn
You will start with the basics of graph theory and gradually move to more advanced topics like graph convolution and self-attention. Hands-on coding exercises and real-world examples help you understand each concept, while step-by-step guidance ensures you can implement and adapt models for your own projects.
Key Features
- Build and train graph neural networks using Python and PyTorch Geometric
- Transform tabular data into graph datasets for advanced analysis
- Apply graph models to tasks like classification, prediction, and anomaly detection
Target Audience
Designed for data scientists and machine learning practitioners with Python experience who want to expand their skills into graph neural networks. If you are looking to solve complex problems using graph data or want to add cutting-edge techniques to your portfolio, this content is a great fit.
