Crash Course on TINTOlib: Tabular Data to Synthetic Images for Vision-Based Machine Learning
This repository provides a comprehensive crash course on using TINTOlib, a Python library designed to transform tabular data into synthetic images for machine learning tasks. It includes slides and Jupyter notebooks that demonstrate how to apply state-of-the-art vision models like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) to problems such as regression and classification, using TINTOlib for data transformation.
The repository also features Hybrid Neural Networks (HyNNs), where one branch is an MLP designed to process tabular data, while another branch—either CNN or ViT—handles the synthetic images. This architecture leverages the strengths of both data formats for enhanced performance on complex machine learning tasks. Ideal for those looking to integrate image-based deep learning techniques into tabular data problems.
This folder contains the examples on how to run TINTOlib in any Python environment.
For this purpose, diferents notebooks have been arranged depending on the Machine Learning problem you want to develop:
- LazyPredict: How to get baseline results with classic models on Tidy Data.
- PyTorch: Recipes for using TINTOlib with PyTorch.
- TensorFlow: Recipes for using TINTOlib with TensorFlow/Keras.
- For more detailed information, refer to the TINTOlib ReadTheDocs.
- GitHub repository: TINTOlib Documentation.
- PyPI: PyPI.
