
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 program 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. Morocco onsite live GPU trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
The trainer really targeted our need to a very specific case study and was able to adapt to the situation (as the solutions to our problematic evolved during the course), beyond the upstream preparation he did.
Anne-Sophie Schwindenhammer
Course: Inkscape
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Dominik Kutten
Course: Adobe LiveCycle Designer
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organization of classes
Jarosław Jasiński
Course: Adobe Illustrator
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knowledge and reactivity to problems
Tadeusz Kopryaniuk
Course: Adobe Illustrator
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all.
Janusz Magnuszewski - mLeasing
Course: Adobe LiveCycle Designer
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work on examples
Kacper Gardziała - Exacto Sp. z o.o.
Course: Adobe Illustrator
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Concentrating on the needs of the trained
Barbara Szubert-Tabaczka - Volkswagen Poznań Sp. z o.o.
Course: Adobe LiveCycle Designer
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Knowledge and the way it is communicated.
Magdalena Rokoszewska - Agencja Kreatywna Przereklamowani
Course: Techniki graficzne (Adobe Photoshop, Adobe Illustrator)
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GPU (Graphics Processing Unit) Subcategories in Morocco
Graphics Processing Unit (GPU) Course Outlines in Morocco
- 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.
- 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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