Блоги

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.

Автор: Robert C. (Intel) Последнее обновление: 31.12.2018 - 16:12
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

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

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

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: 15.10.2019 - 16:50
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: 15.10.2019 - 16:50
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: 15.10.2019 - 16:50