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*
  • Inteligencia artificial
  • 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
  • Inteligencia artificial
  • 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™
  • TensorFlow
  • timeline
  • JSON
  • Depuración
  • Herramientas de desarrollo
  • Aprendizaje mecánico
  • Inteligencia artificial
  • 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
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