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).
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/07/06 - 16:40

Baidu Deep Neural Network Click-Through Rate on Intel® Xeon® Processors E5 v4

How do new web sites selling products or services appear at the top of the search list? The key is to use the right keywords that people might use to search for their products or services. Baidu1 is the most popular search engine in China. Ad companies can pay Baidu so that their ads appear at the top of the search list.
作者: Nguyen, Khang T (Intel) 最后更新时间: 2019/07/05 - 14:36

Intel® Math Kernel Library for Deep Learning Networks: Part 1–Overview and Installation

Learn how to install and build the library components of the Intel MKL for Deep Neural Networks.
作者: Bryan B. (Intel) 最后更新时间: 2019/03/11 - 13:17

BigDL – Scale-out Deep Learning on Apache Spark* Cluster

Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
作者: Sunny G. (Intel) 最后更新时间: 2019/03/11 - 13:17

Deploying BigDL on Microsoft’s Azure* Data Science Virtual Machine

Automated Installation of BigDL Using Deploy to Azure*
作者: 最后更新时间: 2019/03/11 - 13:17

Building Large-Scale Image Feature Extraction with BigDL at

This article shares the experience and lessons learned from Intel and JD teams in building a large-scale image feature extraction framework using deep learning on Apache Spark* and BigDL.
作者: Jason Dai (Intel) 最后更新时间: 2019/05/30 - 15:56

Accelerating Deep Learning Training with BigDL and Drizzle on Apache Spark*

In recent years, the scale of datasets and models used in deep learning has increased dramatically. Although larger datasets and models can improve the accuracy in many artificial intelligence (AI) applications, they often take much longer to train on a single machine.
作者: 最后更新时间: 2019/03/11 - 13:17

Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 2

In this article I will cover the steps required to create the dataset required to train the model using the network we defined in the last tutorial.
作者: Milton-Barker, Adam 最后更新时间: 2019/06/07 - 16:47

Getting Started with Intel® Optimization for PyTorch* on Second Generation Intel® Xeon® Scalable Processors

Accelerate deep learning PyTorch* code on second generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost.
作者: Nathan Greeneltch (Intel) 最后更新时间: 2019/08/15 - 12:50

Intel® CPU Outperforms NVIDIA* GPU on ResNet-50 Deep Learning Inference

Intel Xeon processor outperforms NVidia's best GPUs on ResNet-50.
作者: Haihao Shen (Intel) 最后更新时间: 2019/05/20 - 15:58