Tutorials and examples¶
Use this directory to select a complete learning path. Where a worked example is available, it is integrated into a single page so setup, execution, output, and interpretation remain in one place.
| Resource | Modality | Type | Status | Coverage |
|---|---|---|---|---|
| Image Classification Explainer | Image | Tutorial | Available | Built-in and custom PyTorch classifiers, segmentation, distances, and image plots. |
| LLM Example | LLM | Worked example | Available | Provider setup, prompt perturbation, embeddings, executed outputs, fidelity metrics, plot interpretation, and embedding comparison. |
| Image Examples | Image | Example collection | Coming soon | Reproducible image-classification examples with stored outputs. |
| Image Editing and Pix2Pix Models | Image generation | Planned example | Under construction | Conditional image-to-image explanation and Pix2Pix-style model analysis. |
| Tabular Examples | Tabular | Example collection | Under construction | Classification, regression, mixed features, and stability comparisons. |
| Text Examples | Text | Example collection | Under construction | Token- and phrase-level explanations for conventional text models. |
| Point-Cloud Examples | Point cloud | Example collection | Under construction | Point grouping, perturbation comparison, and 3D attribution. |
Recommended starting points¶
- New users should begin with the installation guide and then choose one of the available resources.
- Users comparing capabilities should first review the explainer directory.
- Contributors preparing a new example should include executable code, environment details, representative output, quality metrics, and a limitations section.