The purpose of this repo is to be the central aggregation, curation, and distribution point for Juypter notebooks that are developed in support of scikit-learn Essentials training programs (e.g., oneAPI Essentials Series).
The Jupyter notebooks are tested and can be run on Intel Devcloud. Below are the steps to access these Jupyter notebooks on Intel Devcloud
- Register on Intel Devcloud
- Go to the "Terminal" in the Intel Devcloud
Code samples are licensed under the MIT license. See License.txt for details.
Third party program Licenses can be found here: third-party-programs.txt
| Notebook Name | Owner | Description |
|---|---|---|
| scikit-learn Essentials Intro | Bob.Chesebrough@intel.com | + Introduction and Motivation for using sklearn algorithms which have have been optimzied in the Intel(r) Extensions for scikit-learn* or its subordinate library, daal4py.. + Explore simple approaches for invoking SYCL context against a multitude of sklearn algorithsm: + + k_means_init_x + + k_means_random + + logistic_regression_lbfgs + + logistic_regression_newton + + dbscan |
| --- | --- | --- |
| sklearn-ex Kmeans | Bob.Chesebrough@intel.com | + Use Data parallel Control (dpCtl) to manage different devices + Use sklearn-ex and daal4py libraries + Explore Kmeans with differing contexts including cpu, gpu and distributed |
| --- | --- | --- |
| Image Clustering | Bob.Chesebrough@intel.com | Use multiple algorthms: + PCA, + kmeans, + DBSCAN all within a given SYCL device context to perform image clustering of a batch of images |
| --- | --- | --- |
| Classifcation of galactic stars using kNN/KDTree | Bob.Chesebrough@intel.com | + What is Sub-Goups and Motivation + Quering for sub-group info + Sub-group collectives + Sub-group shuffle operations |