基于英特尔® 至强 E5 系列处理器的单节点 Caffe 评分和训练

As Deep Neural Network (DNN) applications grow in importance in various areas including internet search engines and medical imaging, Intel teams are working on software solutions to accelerate these workloads that will become available in future versions of Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL). This technical preview demonstrates...
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/03/11 - 13:17

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®...
作者: Sunny G. (Intel) 最后更新时间: 2018/05/08 - 10:50

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.
作者: Sunny G. (Intel) 最后更新时间: 2019/07/05 - 19:13

英特尔® 至强融核™ 处理器如何为机器学习/深度学习应用和框架提供强大优势

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: 2018/07/10 - 08:08

R 语言中的OpenBLAS*和英特尔® 数学核心函数库的性能比较

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: 2019/07/06 - 16:30

利用英特尔® 数据分析加速库提高 Python* 语言中朴素贝叶斯算法的性能

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: 2019/07/06 - 16:30

英特尔® 至强融核™ 处理器针对深度学习提供了出色的性能 - 正在迅速完善性能

Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning (DL) applications. The goal is to provide a standard benchmark to evaluate different hardware platforms using the vendor’s DL libraries.
作者: Andres Rodriguez (Intel) 最后更新时间: 2018/01/24 - 15:35

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.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2018/12/12 - 18:00

英特尔® 深度学习 SDK 教程:安装指南

This tutorial provides step-by-step on Installing the Intel® Deep Learning SDK Training Tool from a Microsoft Windows*, Apple macOS* or Linux Machine.
作者: 管理 最后更新时间: 2018/01/24 - 15:35

案例研究 - 利用英特尔® 深度学习 SDK 训练图像识别模型

本篇案例研究不仅介绍了 LeNet*(一种进行手写数字识别的重要图像识别拓扑),还展示了如何利用训练工具在面向英特尔® 架构优化的 Caffe* 上对混合国家标准技术研究所 (MNIST) 数据集进行可视化设置、调试和训练。目标受众是数据科学家。
作者: Meghana R. (Intel) 最后更新时间: 2018/01/24 - 15:35