In this workshop, we will explore Intel Extensions for Scikit-Learn* and the application of the compute follows data paradigm to extend our learning from the first workshop. We will cover how to apply "Compute Follows Data" approach of the dpctl control library to perform functions such as kmeans, dbscan, pca, ridge, NearestNeighbor, and more on Intel GPU. We will explore how to apply these in more complex use cases such as image clustering and galaxy classification where multiple sklearn functions are computed on the gpu.
- Module 5 – Same as above applied to GPU
- Exploration of KNN on Forest coverage dataset GPU
- Patching for Unsupervised learning Kmeans on GPU
- Patching for Supervised learning Gallery of functions on GPU
- Module 6 – Practicum Optional: Image clustering example – PCA, Kmeans, DBSCAN on GPU
- Module 7 – Practicum: Optional: Synthesized galaxies collide, KNeighbors Classifier, Random Forest on GPU