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 Clermont-Ferrand or in NobleProg corporate training centers in Clermont-Ferrand.
NobleProg -- Your Local Training Provider
Clermont-Ferrand, ZI Le Brézet
NobleProg Clermont-Ferrand, 34, Rue Jules Verne , Clermont-Ferrand, france, 63000
Training courses can be organised at our premises in Clermont-Ferrand, at your premises, or remotely.
Training courses are ...
Training courses can be organised at our premises in Clermont-Ferrand, at your premises, or remotely.
Training courses are given in English or French. We organise a coordination meeting with the trainer and the participants to adapt the training program to your expectations. The courses consist of interactive discussions, theoretical parts and practical exercises.
The training is available as "live on-site training" or "live remote training". Live on-site training can be conducted locally at the client's premises in Clermont or at the NobleProg corporate training centers in Clermont-Ferrand.
Remote e-learning is conducted using an interactive desktop computer. NobleProg is the publisher of the DaDesktop software, which is presented in a video at https://www.dadesktop.com/
This instructor-led, live training in Clermont-Ferrand (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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