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 Leeds or in NobleProg corporate training centers in Leeds.
NobleProg -- Your Local Training Provider
Leeds
No 2 Wellington Place, Leeds, united kingdom, LS1 4AP
This centre is positioned in the middle of Leeds in Wellington Place, which covers 21 acres and will ultimately provide ov...
This centre is positioned in the middle of Leeds in Wellington Place, which covers 21 acres and will ultimately provide over three million square feet of offices, residential, hotel, retail and leisure facilities. Extensive River frontage, inspiring public spaces and piazzas and galleries add to its appeal. This development on a grand scale is designed to act as a catalyst for securing Leeds' status as the country's second commercial centre. The vision is to provide an environment that will draw the businesses of the future to this already thriving city. Leeds is already the UK's largest centre for financial and business services outside London with over 30 national and international banks based in the city. Over 120,000 people are employed in knowledge-intensive occupations, making it the largest centre outside the capital for knowledge-based industries, too. It is also known as a top legal centre and has the country's third largest manufacturing base.
This instructor-led, live training in Leeds (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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