In Part 2 of this workshop series, learn how to speed up key machine-learning algorithms that rely on scikit-learn* and get faster results without specialized hardware.
This session builds on Part 1, extending the "compute follows data" method with Intel® Extension for Scikit-learn* to focus on current and upcoming Intel® GPUs.
In this session:
- Practice what you learned in Part 1 to speed up two code examples: image clustering and galaxy collision.
- Revisit patching strategies to ensure your Intel®-optimized scikit-learn code runs faster than stock scikit-learn.
- Apply patching strategies with a coarse-grained approach down to surgical-level precision.
- Use special techniques to perform scikit-learn computation on Intel GPUs.
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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