
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. Onsite live GPU training can be carried out locally on customer premises in Indonesia or in NobleProg corporate training centers in Indonesia.
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
Thank you for this training. It was a wonderfully practical course - both personally and professionally. I will take away lots of things that I can quickly and easily apply. Great presentation style with lots of opportunities to ask questions and talk about real life examples made it really enjoyable.
Course: Raster and Vector Graphics (Adobe Photoshop, Corel Draw)
Thank you for this training. It was a wonderfully practical course - both personally and professionally. I will take away lots of things that I can quickly and easily apply. Great presentation style with lots of opportunities to ask questions and talk about real life examples made it really enjoyable.
Course: Raster and Vector Graphics (Adobe Photoshop, Corel Draw)
GPU (Graphics Processing Unit) Subcategories in Indonesia
Graphics Processing Unit Course Outlines in Indonesia
- 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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