Analytics Speed with Ease: Visual Bag-Of-Words in Python* with Intel® Data Analytics Acceleration Library (Intel® DAAL) High Level APIIn the companion article, we concluded that Intel® Data Analytics Acceleration Library (DAAL) efficiently utilizes all resources of your machine to perform faster analytics. Now we will show you how to take advantage of these faster analytics methods with simpler Python* commands, namely with Daal4py interface.
Visual Bag-Of-Words in Python*: Speed Advantage of Intel® Data Analytics Acceleration Library (Intel® DAAL) over Scikit-learn*Image recognition with machine learning techniques has achieved significant growth due to advances in recent years in both algorithmic efficiency and hardware performance. Even with these advances, image pre-processing of raw images remains a critical step, especially in larger datasets.
This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
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