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Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 06.07.2019 - 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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 06.07.2019 - 16:30
Видео

Analytics Zoo Overview

This video animation provides an overview of the features and benefits of Analytics Zoo – a unified analytics and AI open source software platform designed to simplify and accelerate AI solutions d

Автор: админ Последнее обновление: 30.05.2019 - 14:00
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Analytics Zoo 概述

此视频动画概述了 Analytics Zoo 的功能和优势。Analytics Zoo 是一个统一的分析和人工智能开源软件平台,旨在使用 Apache Spark* 中的各种框架模型简化和加速人工智能解决方案的开发。它描述了当今行业面临的组织基础设施挑战和人工智能开发效率低下问题。

Автор: админ Последнее обновление: 30.05.2019 - 14:00
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Getting Started with Intel® Optimization for PyTorch* on Second Generation Intel® Xeon® Scalable Processors

Accelerate deep learning PyTorch* code on second generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost.
Автор: Nathan Greeneltch (Intel) Последнее обновление: 15.08.2019 - 12:50
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Getting started with the Intel® DevCloud

Hello all! Happily, this article refers to the Intel® DevCloud! If you don't have access, sign up for it now.

Автор: Karl Fezer (Intel) Последнее обновление: 05.09.2019 - 14:37
Блоги

Intel® CPU Excels in MLPerf* Reinforcement Learning Training

Today, MLPerf* consortium, a group of 40 companies and university research institutes, published the 2nd round of the benchmark results based upon ML

Автор: Koichi Yamada (Intel) Последнее обновление: 30.09.2019 - 16:50