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 trainings in Glasgow can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Glasgow
69 Buchanan St, Glasgow, united kingdom, G1 3HL
The Buchanan Street Centre is located in the heart of Glasgow, in Scotland's most famous shopping and retail district. A m...
The Buchanan Street Centre is located in the heart of Glasgow, in Scotland's most famous shopping and retail district. A major feature of this stunning large Victorian building is the impressive sandstone entrance archway giving access to four floors. Glasgow's fantastic array of shops, hotels, restaurants and bars are on the doorstep and Buchanan Street itself is home to a number of exclusive shops and recently won the Academy of Urbanism 'Great Street Award'. One of the fastest growing cities in the UK, Glasgow is Scotland's largest urban economy and is dominated by financial and business services, public administration, health and retail. It is one of Europe's 16 largest financial centres with a growing number of 'blue-chip' financial sector companies establishing significant operations or headquarters in the city. It also forms the western part of Scotland's 'Silicon Glen' hi-tech sector.
This instructor-led, live training in Glasgow (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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