CUDA (Compute Unified Device Architecture) Training in Durban
Whether delivered online or on-site, these instructor-led live CUDA (Compute Unified Device Architecture) training courses utilize interactive, hands-on exercises to demonstrate how to leverage CUDA-enabled GPUs for general-purpose processing.
CUDA training is offered as either "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is facilitated through an interactive, remote desktop. Onsite live training can be conducted locally at your customer premises in Durban or within NobleProg's corporate training centers in Durban.
NobleProg -- Your Local Training Provider
Garden Court South Beach
Durban, South Africa
Way to get there
From Durban city centre: follow the beachfront/Marine Parade south toward South Beach.
By car/taxi: give the driver the address 73 O.R. Tambo Parade, South Beach, Durban 4001.
From King Shaka International Airport: the hotel is roughly 30 km ;from the airport.
This instructor-led, live training in Durban (online or onsite) is aimed at beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.
By the end of this training, participants will be able to:
Understand the architecture, components, and capabilities of CUDA.
This instructor-led live training in Durban (online or onsite) targets C++ developers who wish to use CUDA to accelerate applications, develop high-performance GPU kernels, and exploit parallel algorithm libraries for scientific computing, data processing, and machine learning workloads.
This instructor-led, live training in Durban (online or onsite) is aimed at C/C++ developers who wish to use CUDA to accelerate compute-intensive applications, including data processing, scientific simulations, machine learning workloads, and image processing pipelines.
This instructor-led, live training in Durban (online or onsite) is aimed at software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.
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