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 Amiens can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Amiens, City Center
Centre International Jules Vernes, 72 rue des Jacobins, Amiens, france, 80000
Nearest airport to the training center: Roissy Charles-de-Gaulle International Airport
Nearest train station: Amiens train s...
Nearest airport to the training center: Roissy Charles-de-Gaulle International Airport
Nearest train station: Amiens train station
Nearest parking lot to training center: Parking EFFIA Amiens The Jacobins
Nearby cafés, restaurants and bars: Restaurant L'adresse, Restaurant From's bistrot à Fromages, Bar Tower's Pub
Training can also be organized on your premises in Strasbourg or remotely. The 3rd option is to rent a room specifically for the duration of the course.
Training sessions can be given in English or French. For each session, we organize a coordination call with the trainer and participants to adapt the program to your expectations and level. Training includes theory, discussion, questions/answers and practical exercises.
Remote training is delivered via DaDesktop for training, a software package published by NobleProg that enables participants to have a workstation (VM) set up specifically for the training.
This instructor-led, live training in Amiens (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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