Online or onsite, instructor-led live Chainer training courses demonstrate through interactive hands-on practice how to use Chainer to build and train neural networks in Python while making the code easy to debug.
Chainer 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. Liverpool onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Liverpool
Horton House, Liverpool, united kingdom, L2 3PF
This business centre is situated in Exchange Flags, a beautifully restored landmark property at the heart of Liverpool's r...
This business centre is situated in Exchange Flags, a beautifully restored landmark property at the heart of Liverpool's rejuvenated business district. This is Liverpool's best office address. The elegance of the original architecture has been matched by a sympathetic yet stylish refurbishment, which has set a benchmark for Grade A office accommodation across the city. Two office buildings flank an impressive bustling open square, which is enclosed by Liverpool Town Hall. Occupiers so far include legal, accountancy, consulting and property firms and The Ministry of Defence. The World Heritage waterfront, a 10,000-seater arena and a conference centre are on the doorstep as well as the Liverpool One shopping and leisure complex. Not since its days as the foremost port in the world has Liverpool witnessed development activity at current levels. This is a city that means business and is attracting investment and visitors from across the world.
This instructor-led, live training in Liverpool (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing neural network models.
Define and implement neural network models using a comprehensible source code.
Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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