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Intel® Data Analytics Acceleration Library

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
Criado por James R. (Blackbelt) Última atualização em 12/12/2018 - 18:00
Article

Migrating Applications from Knights Corner to Knights Landing Self-Boot Platforms

While there are many different programming models for the Intel® Xeon Phi™ coprocessor (code-named Knights Corner (KNC)), this paper lists the more prevalent KNC programming models and further discusses some of the necessary changes to port and optimize KNC models for the Intel® Xeon Phi™ processor x200 self-boot platform.
Criado por Michael Greenfield (Intel) Última atualização em 06/07/2019 - 16:40
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Criado por Última atualização em 06/07/2019 - 16:40
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.
Criado por Sunny G. (Intel) Última atualização em 08/05/2018 - 10:50
Article

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.
Criado por Nguyen, Khang T (Intel) Última atualização em 09/05/2019 - 13:08
Article

面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Criado por Última atualização em 06/07/2019 - 16:40
Article

安装英特尔® Theano*软件优化包和支持工具

Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel®...
Criado por Sunny G. (Intel) Última atualização em 08/05/2018 - 10:50
Article

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.
Criado por Nguyen, Khang T (Intel) Última atualização em 12/12/2018 - 18:00
Article

Intel® Distribution for Python 2017 Update 2 accelerates five key areas for impressive performance gains

Intel Corporation is pleased to announce the release of Intel® Distribution for Python* 2017 Update 2, which offers both performance improvements and new features. 

Criado por Sergey Maidanov (Intel) Última atualização em 25/05/2018 - 11:00
Mensagem de blog

英特尔® 数据分析加速库

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
Criado por James R. (Blackbelt) Última atualização em 12/12/2018 - 18:00