As a general purpose machine-learning library for classification, regression, and clustering algorithms, scikit-learn* has many real-world applications. For example, support-vector machines (SVMs), random decision forests, gradient boosting, K-means clustering, and the density-based spatial clustering of applications with noise (DBSCAN), which is a data-clustering algorithm.

Unfortunately, the scikit-learn library by Python* is inherently slow on hardware without additional software optimizations.

Enter the Intel® Extension for Scikit-learn*, part of the Intel® oneAPI AI Analytics Toolkit. In this webinar, AI technical consulting engineer Rachel Oberman discusses this library, including:

  • How it can speed up scikit-learn in just two lines of code, delivering at least 2x better performance
  • How to improve scikit-learn memory access
  • An overview to get started and a census use case

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Other Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI including developer opportunities and benefits.
  • Subscribe to Code Together— an interview series that explores the challenges at the forefront of cross-architecture development. Each biweekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Available wherever you get your podcasts.

 

 

Intel® oneAPI AI Analytics Toolkit

Accelerate end-to-end machine learning and data science pipelines with optimized deep learning frameworks and high-performing Python* libraries.

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