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. Onsite live Chainer training can be carried out locally on customer premises in York or in NobleProg corporate training centers in York.
NobleProg -- Your Local Training Provider
York
15 Priory Street, York, united kingdom, YO1 6ET
Accessible conference space centrally located in the heart of York,within easy walking distance from York railway station.
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Accessible conference space centrally located in the heart of York,within easy walking distance from York railway station.
Standard lunch is a mixture of vegetarian and non-vegetarian food, which can be tailored to individual requirements and we can offer catering for a wide range of specialist dietary, cultural or religious needs. Where possible, we ask our suppliers to source fair trade or local produce that is organic, free range and GM free.
Address: 15 Priory Street, York, YO1 6ET
Telephone: 01904 621133
Web: http://www.priorystreetcentre.org.uk/
York is a walled city in northeast England that was founded by the ancient Romans. The city offers a wealth of historic attractions, of which York Minster is the most prominent, and a variety of cultural and sporting activities making it a popular tourist destination for millions.
This instructor-led, live training in York (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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