GPU Training Courses

GPU Training Courses

Lokale, door een instructeur geleide, live GPU-trainingen (Graphics Processing Unit) demonstreren door middel van interactieve discussies en hands-on de basisbeginselen van GPU en het programmeren van GPU's. GPU-training is beschikbaar als 'live training op locatie' of 'live training op afstand'. Onsite live training kan lokaal worden uitgevoerd op het terrein van de klant in Nederland of in NobleProg bedrijfsopleidingscentra in Nederland . Live-training op afstand wordt uitgevoerd via een interactieve, externe desktop. NobleProg - uw lokale trainingsaanbieder

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Graphics Processing Unit subcategorieën

GPU Cursusoverzicht

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.
21 hours
Overview
This instructor-led, live training course in Nederland 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 Nederland (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 hours
Overview
This instructor-led, live training in Nederland (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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