天津现场GPU培训

天津现场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天津或者在NobleProg的企业培训中心天津 。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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天津 - 天津中心

天津市天津中心8楼

天津, Tianjin 南京路219号 和平区 300061 ,
天津中心
在天津的培训中心学习GPU。天津中心在和平区南京路和贵阳路的交界,处于天津市中心商务、商业区的核心位置。天津是中国第六大城市,距北京车程仅两小时,坐高速列车仅半小时。天津滨海国际机场是中国北方第二大机场,仅次于北京首都国际机场,从早9点到晚10点,每半小时就有巴士从北京国际机场到天津滨海国际机场。 天津中心由双塔组成:东塔的最顶部几层为服务式公寓,而西塔则包括5星级莱福士酒店和高级办公区域;两塔由豪华购物中心相连。 查看更多

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天津现场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 天津 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 天津 (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 天津 (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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