## Фильтры

Article

### 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
Article

### 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.

Автор: Vadim Pirogov (Intel) Последнее обновление: 21.03.2019 - 12:00
Article

### 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
Article

### Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

Overview
Автор: JON J K. (Intel) Последнее обновление: 30.05.2018 - 07:00
Article

### 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。

Автор: Vadim Pirogov (Intel) Последнее обновление: 21.03.2019 - 12:08
Article

### 面向 GEMM 引入新封装的 API

1     面向 GEMM 引入新封装的 API
Автор: Gennady F. (Blackbelt) Последнее обновление: 05.07.2019 - 19:03
Article

### 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.

Автор: Liu, Mark (Intel) Последнее обновление: 03.07.2019 - 20:07
Article

### 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.

Автор: Gennady F. (Blackbelt) Последнее обновление: 21.03.2019 - 03:01
Article

### 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*
Автор: Nathan Greeneltch (Intel) Последнее обновление: 31.07.2019 - 12:11
Article

### 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

Автор: Nathan Greeneltch (Intel) Последнее обновление: 09.08.2019 - 02:02