Accelerate ML Workloads: Pairwise Distance and Black-Scholes Algorithms

Live Virtual Workshop
Tuesday, June 21 from 09:00am to 11:00am PDT

Claim your spot at this live virtual workshop – Accelerate ML Workloads: Pairwise Distance and Black-Scholes Algorithms

Using the Intel® oneAPI AI Analytics Toolkit, discover efficient methods for implementing the Pairwise and Black-Scholes algorithms.

The Pairwise distance applications takes a set of multi-dimensional points and computes the Euclidean distance, cosine distance, or both, between every pair of points.

The Black-Scholes algorithm computes the price of a portfolio of options using partial differential equations. Parallel computations calculate each option price independently of the others.

Live sample code on Intel® DevCloud (you will need an account to participate) will show how these algorithms are implemented, using the Intel® oneAPI AI Analytics Toolkit for calculations. Topics covered during the workshop include:

  • A walk-through of the code implementing the Pairwise and Black-Scholes algorithms
  • An introduction to the Intel® Extension for Scikit-Learn*
  • An examination of the cosine distance algorithm using Pairwise and Intel Extension for Scikit-learn
  • Visualization of the Pairwise and Black-Scholes algorithms using matplotlib
  • Compile and execute the same algorithms code sample on CPU and GPU offload

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Presenter

Praveen K Kundurthy

Developer Evangelist for AI and oneAPI

Q&A moderator

Bob Chesebrough

Developer Evangelist for AI and oneAPI

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