Registration has begun for this virtual workshop: Accelerate ML Workloads: K-Means and GPairs Algorithms

Claim your spot at this live virtual workshop – Accelerate ML Workloads: K-Means and GPairs Algorithms

Venture deeper into effective methods to accelerate ML workloads. Uncover new way to harness the K-Means and GPairs algorithms.

The K-means clustering algorithm partitions observations from a dataset into a requested number of geometric clusters of points closest to the cluster’s center of mass. Using an existing estimate of the centroids, the algorithm iteratively updates the positions of the centroids until a fixed point is found.

The GPairs distance application takes a set of multi-dimensional points and computes the Euclidean distance between each pair.

Live sample code on Intel® DevCloud will show how these algorithms are implemented, using the Intel® oneAPI AI Analytics Toolkit for calculations. Topics covered during the workshop include:

Explore the code for implementing the K-Means and GPairs algorithms
Learn more about the Intel® Extension for Scikit-learn* at an introductory level
Survey the code implementing the optimized K-Means algorithm using Intel Extension for Scikit-learn
Conduct a walk-through of the code and visualization of the K-Means and GPairs algorithms using matplotlib
Compile and execute the same algorithms code sample on CPU and GPU offload

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Accelerate ML Workloads: K-Means and GPairs Algorithms

Claim your spot at this live virtual workshop – Accelerate ML Workloads: K-Means and GPairs Algorithms

Contact Information

Praveen K Kundurthy

Bob Chesebrough