Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

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.
Authored by Gennady F. (Blackbelt) Last updated on 07/05/2019 - 14:54
Article

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 07/06/2019 - 16:40
Article

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).
Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 16:40
Article

How to Install the Python* Version of Intel® Data Analytics Acceleration Library (Intel® DAAL) in Linux*

The Intel® Data Analytics Acceleration Library (Intel® DAAL) 1, 2 is a software solution for data analytics. It provides building blocks for data preprocessing, transformation, modeling, predicting, and so on.
Authored by Nguyen, Khang T (Intel) Last updated on 07/05/2019 - 19:05
Blog post

Announcing the Intel® Distribution for Python* Beta

The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences.

Authored by Robert C. (Intel) Last updated on 12/31/2018 - 16:12
Article

Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

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.
Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 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.
Authored by Last updated on 07/06/2019 - 16:40
Article

Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

Authored by Vadim Pirogov (Intel) Last updated on 03/21/2019 - 12:00
Article

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 07/06/2019 - 16:40
Article

Vector API Developer Program for Java* Software

This article introduces Vector API to Java* developers. It shows how to start using the API in Java programs, and provides examples of vector algorithms. It provides step-by-step details on how to build the Vector API and build Java applications using it. It provides the location for downloadable binaries for Project Panama binaries.
Authored by Neil V. (Intel) Last updated on 07/06/2019 - 16:30