深圳现场GPU培训

深圳现场GPU培训

本地,讲师指导的现场GPU(图形处理单元)培训课程通过交互式讨论和动手实践演示了GPU的基本原理以及如何编程GPU。 GPU培训可用作“现场实时培训”或“远程实时培训”。现场培训可以在当地的客户场所进行深圳或者在NobleProg的企业培训中心深圳 。远程实时培训通过交互式远程桌面进行。 NobleProg - 您当地的培训机构

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深圳 - 新世界中心

新世界中心

深圳, Guangdong 新世界中心23楼, 益田路6009号 福田区 518035 ,
深圳 - 新世界中心
在深圳的培训中心学习GPU。深圳新世界中心23楼,一个位于福田区核心地段的地标性建筑。该大楼包括一、二层银行机构,多功能会议厅和星级酒店。中心紧邻市政府和金融机构,为各个企业的运作提供了便捷高效的支持。新世界中心距离大陆-香港过境口岸皇岗仅10分钟车程。独特的位置也吸引了来自各个行业企业的入驻,如IT、媒体和贸易行业。此外,三星、香港英皇娱乐集团、深圳报业集团等也在此设立了办公室。 查看更多

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Graphics Processing Unit (GPU)子类别

深圳现场GPU课程大纲

课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
14小时
课程概览
This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.
21小时
课程概览
This instructor-led, live training course in 深圳 covers how to program GPUs for parallel computing, how to use various platforms, how to work with the CUDA platform and its features, and how to perform various optimization techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.
14小时
课程概览
This instructor-led, live training in 深圳 (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.
14小时
课程概览
This instructor-led, live training in 深圳 (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.
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