基于英特尔® 至强™ 处理器 E5 产品家族的多节点分布式内存系统上的 Caffe* 培训

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:55

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.
作者: 最后更新时间: 2019/07/06 - 16:40

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

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

针对英特尔® 至强™ 处理器 E5 系列的 Caffe* 评分优化

为了不断优化英特尔® 架构的深度学习工作负载,我们的工程师探索不同的路径,以达到最高性能。

作者: Gennady F. (Blackbelt) 最后更新时间: 2019/03/21 - 12:28

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

网络研讨会:深度学习 101

深度神经网络具备出色的高级表达功能,并在诸多领域展示了一流的准确性,如计算机视觉、语音识别、自然语言处理以及各种数据分析领域。 深度网络需要训练大量的计算。 英特尔正在优化常见的框架,如 Caffe*、TensorFlow* 和 Theano* 等,以显著提升性能,缩短单个节点的训练总时间。 英特尔还在增强上述框架的多节点分布式训练功能,以在多个节点间分享计算要求,进一步缩短训练时间。

作者: IDZSupport K. 最后更新时间: 2019/01/17 - 02:17

如何安装 Python* 版英特尔® 数据分析加速库(英特尔® 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: 2018/07/13 - 14:32