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. Exeter onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Exeter
The Senate, Southernhay Gardens, Exeter, united kingdom, EX1 1UG
The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exete...
The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exeter Central and Exeter St Davids, which provide regular rail services to all major UK cities. Exeter has been identified by as one of the top 10 most profitable business locations and the county of Devon is regularly voted as number one in England for quality of life.
The Senate is the finest office building in Exeter and offers the largest available floors. This exceptional grade A building is adjacent to the city’s new Crown Court and only a short stroll from the vibrant heart of the city centre, with its bars, restaurants and recently completed Princesshay shopping centre.
The building provides over 72,000 sqft of high specification office accommodation over 5 floors and incorporates underground secure parking for 72 cars. As you would expect from a building of this calibre, the specification and finish reflect a commitment to the highest standards. Efficient space management is expressed in the flexibility of the floorplates, which can be tailored to the needs of each occupier.
This instructor-led, live training in Exeter (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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