GPU 교육

GPU 교육

현지 강사 주도 라이브 GPU (그래픽 처리 장치) 교육 과정은 대화식 토론과 실습을 통해 GPU의 기본 사항 및 GPU 프로그래밍 방법을 보여줍니다. GPU 교육은 "현장 실습"또는 "원격 실습"으로 제공됩니다. 현장 실습은 고객 구내에서 현지에서 실시 할 수 있습니다. 대한민국 또는 NobleProg 기업 교육 센터에서 대한민국 . 원격 라이브 교육은 대화 형 원격 데스크톱을 통해 수행됩니다. NobleProg - 지역 교육 제공자

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Graphics Processing Unit (GPU)서브 카테고리

GPU코스 개요

Course Name
Duration
Overview
Course Name
Duration
Overview
14 시간
Overview
병렬 컴퓨팅을위한 GPU 프로그래밍 방법을 다룹니다. 일부 응용 프로그램에는 심층 학습, 분석 및 엔지니어링 응용 프로그램이 포함됩니다.
14 시간
Overview
CUDA (Compute Unified Device Architecture) is a parallel computing platform and API created by Nvidia.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.

By the end of this training, participants will be able to:

- Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
- Create, compile and launch custom CUDA kernels.
- Manage GPU memory.
- Convert a CPU based application into a GPU-accelerated application.

Format of the Course

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.
21 시간
Overview
이 강사가 진행하는 실습 교육 과정에서는 병렬 컴퓨팅을위한 GPU 프로그래밍 방법, 다양한 플랫폼 사용 방법, CUDA 플랫폼 및 기능 사용 방법, CUDA를 사용하여 다양한 최적화 기법을 수행하는 방법에 대해 설명합니다. 일부 응용 프로그램에는 심도있는 학습, 분석, 이미지 처리 및 엔지니어링 응용 프로그램이 포함됩니다.
14 시간
Overview
Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc.

This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.

By the end of this training, participants will be able to:

- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.

Format of the Course

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

향후GPU 코스

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코스 프로모션

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