How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
Get recipes for installing development tools and libraries on various platforms for the Python library.
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.
Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel®...
如何面向英特尔® 架构优化 Caffe*，训练深度网络模型及部署网络。
This article describes a common type of regression analysis called linear regression and how the Intel® Data Analytics Acceleration Library helps optimize this algorithm on Intel® Xeon® processors.
Speed up your machine learning application code and turn data into insight and actionable results.
Intel® DAAL is a part of Intel® Parallel Studio XE 2016, a developer toolkit for HPC and technical computing applications. Intel® DAAL is a powerful library for big data developers that turns large data clusters into meaningful information with advanced analytics algorithms. In this tutorial, we will see how to build and run Intel® DAAL C++ examples included in the package.
Intel® Data Analytics Acceleration Library (Intel® DAAL) is a software solution that offers building blocks covering all the stages of data analytics, from preprocessing to decision making. The beta version of Intel DAAL 2017 provides support for the Python* language.