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Recipe: Building and running NEMO* on Intel® Xeon Phi™ Processors

The NEMO* (Nucleus for European Modelling of the Ocean) numerical solutions framework encompasses models of ocean, sea ice, tracers, and biochemistry equations and their related physics.This recipe shows the performance advantages of using the Intel® Xeon Phi™ processor 7250.
Authored by Dmitry K. (Intel) Last updated on 03/27/2017 - 16:04
Article

Intel C and C++ Compilers: Features and Supported Platforms

Intel® C++ Compiler Features Supported in Different Products
Authored by Jennifer J. (Intel) Last updated on 03/27/2017 - 15:20
Article

Intel® XDK FAQs - General

Provides frequently asked questions (FAQs) related to developing apps with the Intel® XDK, such as how to get started as a new user, installing more than one version of Intel XDK, signing an app and updating or uninstalling Intel XDK. It also covers questions related to the built-in Brackets editor, differences between mobile platforms, specifying app settings, and other topics.
Authored by Anusha M. (Intel) Last updated on 03/27/2017 - 14:38
Article

Introducing DNN primitives in Intel® Math Kernel Library

    Deep Neural Networks (DNNs) are on the cutting edge of the Machine Learning domain.

Authored by Vadim Pirogov (Intel) Last updated on 03/27/2017 - 14:14
Article

Training and Deploying Deep Learning Networks with Caffe* Optimized for Intel® Architecture

Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL) 2017 optimized for Advanced Vector Extensions (AVX)-2 and AVX-512 instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors (among others). This...
Authored by Andres R. (Intel) Last updated on 03/27/2017 - 14:11
Article

Case Study – Using the Intel® Deep Learning SDK for Training Image Recognition Models

In this case study, we explore LeNet*,one of the prominent image recognition topologies for handwritten digit recognition, and show how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
Authored by Meghana R. (Intel) Last updated on 03/27/2017 - 14:07
Article

Intel® Deep Learning SDK Tutorial: Getting Started with Intel® Deep Learning SDK Training Tool

The Training Tool is a web application running on a Linux* server and provides a user-friendly, intuitive interface for building and training deep learning models. With the Intel Deep Learning SDK Training Tool, you can easily prepare training data, design models, and train models with automated experiments and advanced visualizations.And secondly, Simplify the installation and usage of popular...
Authored by admin Last updated on 03/27/2017 - 14:04
Article

Demo: Software Defined Visualization Using Intel® Xeon Phi™ Processor

In this demo we are showcasing the use of Intel® Xeon Phi™ processor, to do a 3D visualization of tumor in a human brain.

Authored by Beenish Z. (Intel) Last updated on 03/27/2017 - 13:26
Article

Intel® XDK Install Instructions

Note that the APIs listed below augment the standard Cordova APIs, both APIs can and should be used in your application. In some cases there is overlap between the Cordova APIs and the intel.xdk APIs; in that case, we recommend you use the Cordova API first and then use the intel.xdk API when the Cordova API either does not provide the desired functionality or provides inadequate functionality.
Authored by admin Last updated on 03/27/2017 - 11:11
Article

Intel® XDK FAQs - Cordova

Provides FAQs about using Apache Cordova* plugin APIs with Intel® XDK apps, such as adding third-party plugins, plugin variables, and selecting the right plugins for your app. It also covers specific questions related to the Admob, In-App-Purchase, Intel App Security, Capture, and Camera plugins.
Authored by Anusha M. (Intel) Last updated on 03/27/2017 - 10:33
For more complete information about compiler optimizations, see our Optimization Notice.