As Deep Neural Network (DNN) applications grow in importance in various areas including internet search engines and medical imaging, Intel teams are working on software solutions to accelerate these workloads that will become available in future versions of Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL). This technical preview demonstrates...
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®...
In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Machine learning can take very large amounts of data to predict possible outcomes with a high degree of accuracy. The second-generation Intel® Xeon Phi processor has the processor performance and memory bandwidth to address complex machine learning applications.
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).
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.
Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning (DL) applications. The goal is to provide a standard benchmark to evaluate different hardware platforms using the vendor’s DL libraries.
To enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.
This tutorial provides step-by-step on Installing the Intel® Deep Learning SDK Training Tool from a Microsoft Windows*, Apple macOS* or Linux Machine.
本篇案例研究不仅介绍了 LeNet*（一种进行手写数字识别的重要图像识别拓扑），还展示了如何利用训练工具在面向英特尔® 架构优化的 Caffe* 上对混合国家标准技术研究所 (MNIST) 数据集进行可视化设置、调试和训练。目标受众是数据科学家。