Description
Graph Neural Networks in Action, Video Edition. This course is a hands-on training course that teaches how to build and deploy advanced graph neural network models for a variety of deep learning applications. The course focuses on building advanced GNNs for recommendation engines, molecular modeling, and more, and covers essential libraries such as PyTorch Geometry, DeepGraph Library, and Alibaba GraphScope for large-scale training. Participants will learn how to design and train their own models, as well as deploy them in operational environments. The course includes hands-on, real-world projects that explore GNN models for node prediction, link prediction, and graph classification. The course shows how to analyze and predict graph-structured data. Participants work with graph convolutional networks, attention networks, and graph autoencoders to perform tasks such as node classification, link prediction, temporal data processing, and object classification. Along the way, they will learn best practices for training and deploying GNNs at scale, all presented in clear, well-explained Python code.
What you will learn
- Training and deploying a neural network.
- Generating Node Embeddings.
- Using GNNs at scale for very large datasets.
- Building a Graph Data Pipeline.
- Create a Graph Data Schema.
- Understanding the Taxonomy of GNNs.
- Manipulating graph data with NetworkX.
This course is suitable for people who:
- Python programmers who are familiar with Machine Learning and Deep Learning Basics.
Course details for Graph Neural Networks in Action, Video Edition
- Publisher: Oreilly
- Instructor: Namid Stillman , Keita Broadwater
- Training level: Beginner to advanced
- Training duration: 11 hours and 2 minutes
Course headings
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
1.7 GB
