In this liveProject, you’ll implement a Graph Neural Network (GNN). This powerful model will allow you to use the document content from the first liveProject combined with the structure of the citation graph from the second liveProject to build an even more powerful model—one that will predict the sub-field of statistics of each of your customers’ papers.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
When you start your liveProject, you get full access to the following books for 90 days.
This liveProject is for Natural Language Processing (NLP) practitioners who have an intermediate level of knowledge of the Python programming language (especially in the NLP domain) and who are ready to uplevel their NLP skills by applying GNNs for document classification. To begin this liveProject, you’ll need to be familiar with the following:
- Intermediate Python
- Intermediate PyTorch
- Basic PyTorch Geometric
- basic NLP and Graph Theory
- intermediate Deep Learning
you will learn
In this liveProject, you’ll learn skills and techniques for using GNNs for classifying the nodes in a graph:
- Building the dataset from the data
- Building the GNN classifier
- Training and evaluating the classifier
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.