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
Machine Translated
회원 평가
예를 들은 라이브, digression, "생활"의 예를 논의했다. 교육의 한 형태, 즉 실용적인 예와 강의를 엮고 이러한 예에 대해 토론하는 것입니다.
Piotr Glazor - Nokia
Course: NVIDIA GPU Programming - Extended
Machine Translated
트레이너는 실제로 매우 구체적인 사례 연구에 대한 필요성을 표적으로 삼았고 상황에 적응할 수있었습니다 (과정에서 진화 된 문제에 대한 해결책으로서)
Anne-Sophie Schwindenhammer
Course: Inkscape
Machine Translated
모든
Dominik Kutten
Course: Adobe LiveCycle Designer
Machine Translated
수업 조직 Jaro
Jarosław Jasiński
Course: Adobe Illustrator
Machine Translated
문제에 대한 지식과 반응 Tadeusz Kopryani
Tadeusz Kopryaniuk
Course: Adobe Illustrator
Machine Translated
다.
Janusz Magnuszewski - mLeasing
Course: Adobe LiveCycle Designer
Machine Translated
예제 작업
Kacper Gardziała - Exacto Sp. z o.o.
Course: Adobe Illustrator
Machine Translated
교육의 필요성에 집중
Barbara Szubert-Tabaczka - Volkswagen Poznań Sp. z o.o.
Course: Adobe LiveCycle Designer
Machine Translated
지식과 그것이 전달 되는 방식.
Magdalena Rokoszewska - Agencja Kreatywna Przereklamowani
Course: Techniki graficzne (Adobe Photoshop, Adobe Illustrator)
Machine Translated
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.
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.
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
2021-02-08 09:30:00
14 hours
2021-02-08 09:30:00
14 hours
2021-02-23 10:00:00
14 hours
2021-02-23 10:00:00
14 hours
2021-03-03 09:30:00
21 hours
Other countries
Consulting
주말GPU (Graphics Processing Unit)코스, 밤의Graphics Processing Unit트레이닝, GPU (Graphics Processing Unit)부트 캠프, GPU (Graphics Processing Unit) 강사가 가르치는, 주말GPU교육, 밤의Graphics Processing Unit (GPU)과정, GPU (Graphics Processing Unit)코칭, Graphics Processing Unit강사, Graphics Processing Unit트레이너, Graphics Processing Unit (GPU)교육 과정, Graphics Processing Unit (GPU)클래스, GPU현장, Graphics Processing Unit (GPU)개인 강좌, GPU (Graphics Processing Unit)1 대 1 교육
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