Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.
Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.
By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

Osoby które kupowały "Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch", wybierały także:

• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zÅ‚, (39,90 zÅ‚ -79%)
• Przywództwo w Å›wiecie VUCA. Jak być skutecznym liderem w niepewnym Å›rodowisku 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Scrum. O zwinnym zarzÄ…dzaniu projektami. Wydanie II rozszerzone 58,64 zÅ‚, (12,90 zÅ‚ -78%)

Spis treści

Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch eBook -- spis treści

• 1. Getting Started with Graph Learning
• 2. Graph Theory for Graph Neural Networks
• 3. Creating Node Representations with DeepWalk
• 4. Improving Embeddings with Biased Random Walks in Node2Vec
• 5. Including Node Features with Vanilla Neural Networks
• 6. Introducing Graph Convolutional Networks
• 7. Graph Attention Networks
• 8. Scaling Graph Neural Networks with GraphSAGE
• 9. Defining Expressiveness for Graph Classification
• 10. Predicting Links with Graph Neural Networks
• 11. Generating Graphs Using Graph Neural Networks
• 12. Learning from Heterogeneous Graphs
• 13. Temporal Graph Neural Networks
• 14. Explaining Graph Neural Networks
• 15. Forecasting Traffic Using A3T-GCN
• 16. Detecting Anomalies Using Heterogeneous Graph Neural Networks
• 17. Building a Recommender System Using LightGCN
• 18. Unlocking the Potential of Graph Neural Networks for Real-Word Applications