The Intel® Data Analytics Acceleration Library (Intel® DAAL) is written on Intel® Architecture optimized building blocks and includes support for all data analytics stages. Data-driven decision making is empowered by DAAL with foundations for data acquisition, preprocessing, transformation, data mining, modeling, validation. Python users can access these foundations with the python API for DAAL (named PyDaal). Machine learning with python gets an injection of power with PyDaal, accessed via a simple scripting API. Furthermore, PyDaal provides the unique capability to easily extend python scripted batch analytics to online (streaming) data acquisition and/or distributed math processing. To achieve best performance on a range of Intel® processors, Intel® DAAL uses optimized algorithms from the Intel® Math Kernel Library and Intel® Integrated Performance Primitives. Intel® DAAL provides APIs for C++, JAVA, and Python. In this Gentle Introduction series, we will cover the basics of PyDaal from the ground up. The first installment will introduce DAAL’s custom data structure, Numeric Table, and data management in the world of PyDaal.
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