Written by Shizuo Kaji
This Jupyter Notebook was originally prepared for the online event:
TDA for Applications: Tutorial and Workshop
held on 18–19 June 2020.
Our main example notebook is designed to run on Google Colaboratory, so you don’t need to set up a Python environment on your computer.
This notebook covers:
- Feature extraction using persistent homology from various types of data: Point clouds, Graphs, Images, Volumes, Time-series data
- Regression and classification using topological features
- Dimension reduction while preserving topological features
- Visualization to reveal the shape of data
CAUTION The following examples are no longer maintained.
They are not compatible with Google Colaboratory.
We demonstrate how deep learning can be combined with persistent homology in this repository:
HomologyCNN
As an example of Natural Language Processing (NLP), we analyze math papers from arXiv.
For setup instructions, refer to the NLP Example Guide.
- Persistent Homology - An Introduction via Interactive Examples:
A quick and interactive introduction to the theory of persistent homology.