GPU Training in London

GPU Training in London

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. Onsite live GPU training can be carried out locally on customer premises in London or in NobleProg corporate training centres in London.

NobleProg -- Your Local Training Provider

London - Victoria

etc Venues Victoria
One Drummond Gate Victoria
London, LND SW1V 2QQ
United Kingdom
,
See map: Google Maps
London GB
London - Victoria
Learn GPU in our training center in London. Purpose built conference, training and meeting venue with private outdoor garden space in Pimlico, Victoria. Superb private courtyard garden ideal for teambuilding and networking. Great location in Victoria at the junction of Drummond Gate and Vauxhall Bridge Road. Adjacent to Pimlico underground station and a short walk from Victoria mainline and underground stations. Read more

London, Hatton Garden

London, Hatton Garden
Etc. Venues 51-53 Hatton Garden
London , LND EC1N 8HN
United Kingdom
,
See map: Google Maps
London GB
London, Hatton Garden
Learn GPU in our training center in London.

Overview

Located near Farringdon and Chancery Lane.

A stylish and impressive eight-storey Art Deco building ideal for hosting a range of training, conferences and meetings near Farringdon and Chancery Lane stations.

Directions

The Hatton is located in London's Diamond District in Hatton Garden.

By Underground, Farringdon Station (Metropolitan, Circle, Hammersmith and City lines)
Follow the...

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London, Marble Arch

Etc Venues - Marble Arch
Garfield House 86 Edgware Road
London, LND W2 2EA
United Kingdom
,
See map: Google Maps
London GB
London, Marble Arch
Learn GPU in our training center in London.

Situated on Edgware Road just minutes away from Marble Arch Station and Bond Street, the venue offers a contemporary designed, high quality conference, training and meeting venue with extensive break out facilities.

Close to the extensive shopping and department stores on Oxford Street. Freshly prepared food is served onsite by our chefs from a theatre style kitchen

By Underground

Central line to Marble Arch Station

When you exit the...

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Testimonials

★★★★★
★★★★★

Graphics Processing Unit (GPU) Subcategories

GPU Course Events - London

GPU Course Outlines in London

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 London 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 London (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 London (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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