The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences.
This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*
本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
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.
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.
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.