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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
Автор: Mike P. (Intel) Последнее обновление: 06.07.2019 - 16:40
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).
Автор: Nguyen, Khang T (Intel) Последнее обновление: 06.07.2019 - 16:40
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.

Автор: Zhang, Zhang (Intel) Последнее обновление: 11.03.2019 - 13:17
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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 05.07.2019 - 19:05
Блоги

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.

Автор: Robert C. (Intel) Последнее обновление: 31.12.2018 - 16:12
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.
Автор: Sunny G. (Intel) Последнее обновление: 05.07.2019 - 19:10
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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 06.07.2019 - 16:40
Блоги

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.
Автор: Pradeep Dubey (Intel) Последнее обновление: 21.03.2019 - 12:40
Article

Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
Автор: Sunny G. (Intel) Последнее обновление: 08.05.2018 - 10:50
Article

Improving Support Vector Machine with Intel® Data Analytics Acceleration Library

Introduction
Автор: Nguyen, Khang T (Intel) Последнее обновление: 07.10.2018 - 07:15