南京现场GPU培训

南京现场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南京或者在NobleProg的企业培训中心南京 。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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南京 - 金陵亚太商务酒店

金陵饭店亚太商务楼

南京, Jiangsu 南京市鼓楼区新街口汉中路2号 8楼 210005 ,
南京-金陵亚太商务酒店
在南京的培训中心学习GPU。金陵饭店亚太商务楼位于市中心的繁华地段——鼓楼区新街口商务中心区。雷格斯商务中心就在这幢商业新地标的第8层。亚太商务楼不仅设有甲级办公楼层,还配套建设了五星级酒店、行政酒廊、饭店和SPA水疗中心。亚太商务楼因其发达的轨道交通条件(地铁1号线和2号线新街口站)和地处主要商业区而颇受IBM、惠普、花旗银行、德勤及西门子等跨国企业的青睐。南京地处东南,是江苏省省会,有十朝古都之誉,因其宝贵的古代文化遗产和壮丽景色而闻名,现已成为中国最繁荣的大都市之一。 查看更多

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Graphics Processing Unit子类别

南京现场GPU (Graphics Processing Unit)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 南京 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 南京 (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 南京 (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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