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A Tutorial on the C++ API of Intel® Data Analytics Acceleration Library

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.
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Tutorial for Intel® DAAL: Using Simple C++ Examples

System Environment

Intel® DAAL version : 2016 Gold Initial Release (w_daal_2016.0.110.exe)

OS : Windows* 8.1

IDE : Visual Studio 2013

 

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Tutorial for Intel® DAAL : Using Simple Java* Examples

System Environment

Intel® DAAL version : 2016 Gold Initial Release (w_daal_2016.0.110.exe)

OS : Windows 8.1

Authored by JON J K. (Intel) Last updated on 07/06/2019 - 11:41
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Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
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Optimize Linear Regression Model with Intel® DAAL

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.
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Intel® Math Kernel Library for Deep Neural Networks: Part 2 – Code Build and Walkthrough

Learn how to configure the Eclipse* IDE to build the C++ code sample, along with a code walkthrough based on the AlexNet deep learning topology for AI applications.
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Distributed Deep Learning with Docker*, Intel® Nervana™ technology, neon™, and Pachyderm*

The recent advances in machine learning and artificial intelligence are amazing! It seems like we see something groundbreaking every day, from self-driving cars, to AIs learning complex games.

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Speed up your machine learning application code and turn data into insight and actionable results.

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This video covers Analytics Zoo Overview. Analytics Zoo provides a unified analytics and AI platform, bringing together Apache Spark*, BigDL, Tensorflow* and Keras.

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