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 Reading or in NobleProg corporate training centers in Reading.
NobleProg -- Your Local Training Provider
Reading
400 Thames Valley Park Drive, Reading, united kingdom, RG6 1PT
The Thames Valley Park business centre is in a prestigious, modern 200-acre business park and nature reserve idyllically posi...
The Thames Valley Park business centre is in a prestigious, modern 200-acre business park and nature reserve idyllically positioned close to the south bank of the River Thames on the east side of Reading. The city has a significant historical involvement in the information technology industry and this attractive and highly sought-after location is home to an impressive array of well-known information technology companies. It is the UK headquarters for a world-leading software giant as well as a number of other household names providing infrastructure services, consulting, e-commerce and computer hardware. Other major businesses based here work in industries ranging from energy to financial services. Close to junction 10 of the M4 motorway, the park is easily accessible by road with a free shuttle bus service to the city's business centre and station, both of which can also be reached by taking a peaceful 15-minute walk along the river.
This instructor-led, live training in Reading (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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