The following Jupyter notebooks will guide you through the most important features of AmpliGraph:
- AmpliGraph basics: training, saving and restoring a model, evaluating a model, discover new links, visualize embeddings. [Jupyter notebook] [Colab notebook]
- Link-based clustering and classification: how to use the knowledge embeddings generated by a graph of international football matches in clustering and classification tasks. [Jupyter notebook] [Colab notebook]
Additional examples and code snippets are available here.