Caso práctico

Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

This paper evaluates the performance of Intel® Xeon® processor powered machines for running deep learning on the GoogleNet* topology (Inception* v3). The functional problem tackled is the identification of power system components such as pylons, conductors, and insulators from the real-world video footage captured by unmanned aerial vehicles (UAVs) or commercially available drones.
  • Profesional
  • Profesores
  • Estudiantes
  • Linux*
  • Inteligencia artificial
  • Python*
  • Intermedio
  • TensorFlow*
  • Unattended Baggage Detection Using Deep Neural Networks in Intel® Architecture

    In a world becoming ever more attuned to potential security threats, the need to deploy sophisticated surveillance systems is increasing. An intellectual system that functions as an intuitive “robotic eye” for accurate, real-time detection of unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public areas. This article discusses inferencing a Microsoft Common Objects in Context (MS-COCO) detection model for detecting unattended baggage in a train station.

  • Profesional
  • Profesores
  • Estudiantes
  • Linux*
  • Inteligencia artificial
  • Intermedio
  • Caffe*
  • Aprendizaje mecánico
  • Seguridad
  • Profiling Tensorflow* workloads with Intel® VTune™ Amplifier

    Machine learning applications are very compute intensive by their nature. That is why optimization for performance is quite important for them. One of the most popular libraries, Tensorflow*, already has an embedded timeline feature that helps understand which parts of the computational graph are causing bottlenecks but it lacks some advanced features like an architectural analysis.

  • Linux*
  • Microsoft Windows* 10
  • Inteligencia artificial
  • C/C++
  • Python*
  • Principiante
  • Intermedio
  • Intel® Parallel Studio XE
  • Amplificador Intel® VTune™
  • VTune
  • TensorFlow
  • timeline
  • JSON
  • Depuración
  • Herramientas de desarrollo
  • Aprendizaje mecánico
  • Finding BIOS Vulnerabilities with Symbolic Execution and Virtual Platforms

    Finding BIOS Vulnerabilities With Excite

    Finding vulnerabilities in code is part of the constant security game between attackers and defenders. An attacker only needs to find one opening to be successful, while a defender needs to search for and plug all or at least most of the holes in a system. Thus, a defender needs more effective tools than the attacker to come out ahead.

    How F5 Networks Profiles for Success

    When Seattle-based F5 Networks, Inc. needed to amp up its BIG-IP DNS* solution for developers, it got help from Intel.

    Business users expect their applications to be fast, secure, and always available. Anything less is unacceptable. That’s why F5 gives the developers who build those applications the tools they need to deliver maximum speed, security, and availability.

  • Intel® Parallel Studio XE
  • Intel® Parallel Studio XE Cluster Edition
  • Intel® Parallel Studio XE Composer Edition
  • Intel® Parallel Studio XE Professional Edition
  • Amplificador Intel® VTune™
  • Novosibirsk State University Gets More Efficient Numerical Simulation

    Russia's Novosibirsk State University boosted a simulation tool’s performance by 3X with Intel® Parallel Studio, Intel® Advisor, and Intel® Trace Analyzer and Collector, cutting the standard time for calculating one problem from one week to just two days.

  • Intel® Parallel Studio XE
  • Computación en paralelo
  • NetUP 利用英特尔® 媒体软件开发套件向全球数百万观众转播里约奥运会赛事

    2016 年 8 月,全球各地的 50 万粉丝来到里约热内卢,观看为期 17 天的夏季奥运会。 同时,全球数百万观众在电视机屏幕前观看了奥运会直播。

    在不同的大陆上进行实况电视转播是一项艰巨的任务,需要可靠的设备和灵活的技术支持。 对全球最大的多媒体新闻机构 - 汤森路透 (Thomson Reuters) 来说,这是个不小的挑战。

    为了迎接挑战,汤森路透选择了 NetUP 作为技术合作伙伴,使用 NetUP 设备进行了从里约热内卢到纽约与伦敦办事处的实况转播。 NetUP 与英特尔携手开发了 NetUP 转码器,使用了英特尔® 媒体软件开发套件,该套件是一款跨平台的 API,用于在 Windows* 上开发媒体应用。

  • Intel® Media SDK
  • Procesamiento de medios
  • Optimización
  • Suscribirse a Caso práctico