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腾讯* 在基于英特尔® 至强® 处理器的游戏内购买推荐系统中使用机器学习

To enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.
Authored by Nguyen, Khang T (Intel) Last updated on 12/12/2018 - 18:00
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Analytics Speed with Ease: Visual Bag-Of-Words in Python* with Intel® Data Analytics Acceleration Library (Intel® DAAL) High Level API

In 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.
Authored by Preethi Venkatesh (Intel) Last updated on 08/27/2019 - 14:30
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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.
Authored by Nathan Greeneltch (Intel) Last updated on 08/27/2019 - 14:30
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Using Intel® MPI Library on Intel® Xeon Phi™ Product Family

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.
Authored by Nguyen, Loc Q (Intel) Last updated on 10/15/2019 - 15:04
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应用蚁群优化算法 (ACO) 实施交通网络扩展

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.
Authored by Sunny G. (Intel) Last updated on 10/15/2019 - 16:40
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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.
Authored by Sunny G. (Intel) Last updated on 10/15/2019 - 16:40
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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
Authored by Mike P. (Intel) Last updated on 10/15/2019 - 16:50
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Intel Solutions and Technologies for the Evolving Data Center

  One Stop for Optimizing Your Data Center From AI to Big Data to HPC: End-to-end Solutions
Authored by admin Last updated on 10/15/2019 - 17:00
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Tencent* Uses Machine Learning for In-Game Purchase Recommendation System on Intel® Xeon® Processors

To enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.
Authored by Nguyen, Khang T (Intel) Last updated on 10/15/2019 - 19:55