GPU Training Courses

GPU Training Courses

Online or onsite, instructor-led live GPU (Graphics Processing Unit) training courses demonstrate through interactive discussion and hands-on practice the fundamentals of GPU and how to programme GPUs.

GPU training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. The UK onsite live GPU trainings can be carried out locally on customer premises or in NobleProg corporate training centres.

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Graphics Processing Unit (GPU) Subcategories

Graphics Processing Unit Course Outlines

Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Overview
This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.
14 hours
Overview
This instructor-led, live training in the UK (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.
21 hours
Overview
This instructor-led, live training course in the UK 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 hours
Overview
This instructor-led, live training in the UK (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.
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