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Article

IDF'15 Webcast: Data Analytics and Machine Learning

This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
作者: Mike P. (Intel) 最后更新时间: 2019/10/15 - 16:50
Article

Performance Comparison of OpenBLAS* and Intel® Math Kernel Library in R

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/12/09 - 11:40
Article

Using Intel Data Analytics Acceleration Library on Apache Spark*

Apache Spark* (http://spark.apache.org/) is a fast and general engine for large-scale data processing.

作者: Zhang, Zhang (Intel) 最后更新时间: 2019/03/11 - 13:17
视频

Intel® Data Analytics Acceleration Library

Kent Moffat, Senior Product Manager at Intel, discusses Intel’s data analytics acceleration and math kernel libraries and how to get started. 

作者: 管理 最后更新时间: 2019/08/27 - 14:30
Article

How to Install the Python* Version of Intel® Data Analytics Acceleration Library (Intel® DAAL) in Linux*

The Intel® Data Analytics Acceleration Library (Intel® DAAL) 1, 2 is a software solution for data analytics. It provides building blocks for data preprocessing, transformation, modeling, predicting, and so on.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/12/09 - 12:20
博客

Announcing the Intel® Distribution for Python* Beta

The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences.

作者: Robert C. (Intel) 最后更新时间: 2019/12/09 - 12:20
Article

Scale-Up Implementation of a Transportation Network Using Ant Colony Optimization (ACO)

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.
作者: Sunny G. (Intel) 最后更新时间: 2019/10/15 - 16:40
Article

Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/12/09 - 12:20
Article

How to Install the Python* Version of Intel® Data Analytics Acceleration Library (Intel® DAAL)

Intel® Data Analytics Acceleration Library (Intel® DAAL) is a software solution that offers building blocks covering all the stages of data analytics, from preprocessing to decision making. The beta version of Intel DAAL 2017 provides support for the Python* language.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/12/09 - 12:20
博客

How Intel® Xeon Phi™ Processors Benefit Machine Learning/Deep Learning Apps and Frameworks

Machine learning can take very large amounts of data to predict possible outcomes with a high degree of accuracy. The second-generation Intel® Xeon Phi processor has the processor performance and memory bandwidth to address complex machine learning applications.
作者: Pradeep Dubey (Intel) 最后更新时间: 2019/10/15 - 18:24