Learn with PyKale#
PyKale Tutorials
Learn with PyKale
Runnable, reproducible tutorials for multimodal learning, transfer learning and interdisciplinary AI — organised by the domain that owns them.
Get started#
Install PyKale. Run
pip install pykale. Setup guideTry the first tutorial. Domain adaptation on digit images is the recommended starting point.
Find your next topic. Browse all tutorials or follow a learning path.
What’s new#
New Domain-based tutorial structure — Browse tutorials by Protein, Cancer, Cardiac or general methods.
New Executable Sphinx-Gallery tutorials — Each tutorial includes a runnable script. Narrated walkthroughs are being completed.
Updated Reference tutorial available — The digit-image domain adaptation walkthrough is ready for first-time readers. Read tutorial
Featured learning paths#
Choose a route, then follow its tutorials in order.
Start here. The gentlest path from a first run to a working domain-adaptation model.
3 tutorials · Starts with Domain adaptation on synthetic data
PyKale’s core strength – aligning models across shifting data distributions.
4 tutorials · Starts with Domain adaptation on digit images
Combining modalities – audio and image, graphs and sequences, multiomics.
3 tutorials · Starts with Audio-visual digit classification
Applications across protein, cancer and cardiac data.
4 tutorials · Starts with Drug-target affinity prediction with DeepDTA
Browse all tutorials#
Filter the full catalogue by domain, task, modality, difficulty, runtime and status.
Filters
No tutorials match these filters.
Contributing#
Start from the templates in source/_shared/templates/.
See which narratives are still drafts and pick one to write.
Something wrong or unclear? Open an issue.
The full contribution workflow and conventions.