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. Edinburgh onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Edinburgh
83 Princes Street , Edinburgh, united kingdom, EH2 2ER
The training rooms is located under a 10 minute walk from Edinburgh Rail Station
This centre's location on Edinburg...
The training rooms is located under a 10 minute walk from Edinburgh Rail Station
This centre's location on Edinburgh's world-famous Princes Street takes some beating. Right in the centre of Scotland's capital city, these prestigious business premises are spread across six floors with fantastic views over Princes Street Gardens towards the Royal Mile and Edinburgh Castle. As well as having all the usual amenities of a major city on its doorstep, the building also has a car park. Prosperous Edinburgh is a global centre for business, science, education and the arts and is said to have the strongest economy of any UK city outside London. Banking has been a part of the economic life of Edinburgh for over 300 years and today it is the UK's second financial centre after London and Europe's fourth by equity assets. Tourism, financial services and banking are particularly important employers as well as education, the public sector and hi-tech research and development.
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This instructor-led, live training in Edinburgh (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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