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Palestra: Como otimizar seu código sem ser um "ninja" em Computação Paralela

Não perca a palestra "Como otimizar seu código sem ser um "ninja" em Computação Paralela" da Intel que será ministrada durante a Semana sobre Programação Massivamente Paralela em Petrópolis, RJ, no Laboratório Nacional de Computação Científica. Data: 02/02/2016 - 11h30 Local: LNCC - Av. Getúlio Vargas, 333 - Quitandinha - Petrópolis/RJ
Authored by Igor F. (Intel) Last updated on 07/06/2019 - 16:40
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Announcing the Intel® Distribution for Python* Beta

The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences.

Authored by Robert C. (Intel) Last updated on 12/31/2018 - 16:12
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Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

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Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 16:40
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利用英特尔® 数据分析加速库提高 Python* 语言中朴素贝叶斯算法的性能

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 16:30
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Intel® Data Analytics Acceleration Library - Decision Trees

Decision trees method is one of most popular approaches in machine learning. They can easily be used to solve different classification and regression tasks.
Authored by Gennady F. (Blackbelt) Last updated on 07/06/2019 - 10:58
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Maximize TensorFlow* Performance on CPU: Considerations and Recommendations for Inference Workloads

This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*
Authored by Nathan Greeneltch (Intel) Last updated on 07/31/2019 - 12:11
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Authored by Nathan Greeneltch (Intel) Last updated on 08/09/2019 - 02:02