北京市朝阳区现场GPU培训

北京市朝阳区现场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北京市朝阳区或者在NobleProg的企业培训中心北京市朝阳区 。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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北京 - Digital 01 Building

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北京 北京市朝阳区光华路丙12号 100000 ,
数码01大厦
在北京市朝阳区的培训中心学习GPU。中国国际贸易中心与嘉里中心之间,背倚国贸中心,与嘉里公寓隔街相望,总建筑面积共32000平方米。 周边配套 商场:北京国贸商城旗舰店、万达新世界商场、贵友商场 银行:广发银行国贸支行、华夏银行京广支行、兴业银行光华路支行、华夏银行光华支行、中信银行财富中心支行 酒店:恋都商旅酒店、如家快捷酒店、建国饭店、万达索菲特大酒店、京伦饭店、北京伯豪瑞廷酒店、北京嘉里大酒店、千禧大酒店 餐饮:祖母的厨房、小王府、恒河印度餐厅、时时乐建国门餐厅等 查看更多

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北京市朝阳区现场Graphics Processing Unit (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 北京市朝阳区 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 北京市朝阳区 (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 北京市朝阳区 (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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