📮 Graph Neural Networks in Action (Final) by Keita Broadwater (.PDF)

A hands-on guide to powerful graph-based Deep Learning models. Graph Neural Networks in Action teaches you to create powerful Deep Learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. Inside this practical guide, you’ll explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. There are now several GNN libraries in the Python ecosystem, including PyTorch Geometric (PyG), Deep Graph Library (DGL), GraphScope, and Jraph. We focus on PyG, which is one of the most popular and easy-to-use frameworks, written on top of PyTorch. The book provides a survey of the most relevant implementations of GNNs, including graph convolutional networks (GCNs), graph autoencoders (GAEs), graph attention networks (GATs), and graph long short-term memory (LSTM). The aim is to cover the GNN tasks mentioned earlier. For Python programmers familiar with Machine Learning and the basics of Deep Learning.

♻️ Book's Info:

📥 Download Links:

https://uploda.sh/p3R5QneYlIS8

https://devuploads.com/da1uqh60upfr