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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.
Autor Nguyen, Khang T (Intel) Última actualización 06/07/2019 - 16:40
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Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

Autor Vadim Pirogov (Intel) Última actualización 21/03/2019 - 12:00
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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.
Autor Nguyen, Khang T (Intel) Última actualización 06/07/2019 - 16:30
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Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

Overview
Autor JON J K. (Intel) Última actualización 30/05/2018 - 07:00
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在英特尔® 数学核心函数库中引入 DNN 基元

    深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用,最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用,达到甚至超过了人类能力。如今,DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题,如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。

Autor Vadim Pirogov (Intel) Última actualización 21/03/2019 - 12:08
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面向 GEMM 引入新封装的 API

1     面向 GEMM 引入新封装的 API
Autor Gennady F. (Blackbelt) Última actualización 05/07/2019 - 19:03
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Intel® Media SDK & Intel® Media Server Studio Historical Release Notes

Release Notes of Intel® Media SDK include important information, such as system requirements, what's new, feature table and known issues since the previous release.

Autor Liu, Mark (Intel) Última actualización 03/07/2019 - 20:07
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Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)

    The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM) functions.

Autor Gennady F. (Blackbelt) Última actualización 21/03/2019 - 03:01
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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*
Autor Nathan Greeneltch (Intel) Última actualización 31/07/2019 - 12:11
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最大限度提升 CPU 上的 TensorFlow* 性能:推理工作负载的注意事项和建议

本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
Autor Nathan Greeneltch (Intel) Última actualización 09/08/2019 - 02:02