Article

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.
Authored by Bryan B. (Intel) Last updated on 03/11/2019 - 13:17
Article

Building Large-Scale Image Feature Extraction with BigDL at JD.com

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.
Authored by Jason Dai (Intel) Last updated on 05/30/2019 - 15:56
File Wrapper

Parallel Universe Magazine - Issue 28, April 2017

Authored by admin Last updated on 09/30/2019 - 16:45
Article

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.
Authored by Nathan Greeneltch (Intel) Last updated on 10/15/2019 - 16:50
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 10/15/2019 - 16:50
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 10/15/2019 - 16:50
Article

Accelerating Deep Learning Based Large-Scale Inverse Kinematics with Intel® Distribution of OpenVINO™ Toolkit

Use Deep Learning Deployment Toolkit (DLDT) to deploy deep-learning algorithms to solve character Inverse Kinematics (IK) problems.
Authored by Tai Ha (Intel) Last updated on 10/15/2019 - 21:09
Blog post

New Deep Learning Workbench Profiler Provides Performance Insights

In recent releases of the Intel® Distribution of OpenVINO™ Toolkit developers can optimize their applications using a suite of Python* calibration tools, namely 

Authored by Shubha R. (Intel) Last updated on 10/17/2019 - 12:38