Learn how to install and build the library components of the Intel MKL for Deep Neural Networks.
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.
Getting Started with Intel® Optimization for PyTorch* on Second Generation Intel® Xeon® Scalable ProcessorsAccelerate deep learning PyTorch* code on second generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost.
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) . It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL)  to improve the performance of an NB algorithm.
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).
Accelerating Deep Learning Based Large-Scale Inverse Kinematics with Intel® Distribution of OpenVINO™ ToolkitUse Deep Learning Deployment Toolkit (DLDT) to deploy deep-learning algorithms to solve character Inverse Kinematics (IK) problems.
In recent releases of the Intel® Distribution of OpenVINO™ Toolkit developers can optimize their applications using a suite of Python* calibration tools, namely