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. Recife onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Recife - JCPM Trade Center
60 Eng. Antonio de Goes Avenue, 7º and 14º floor, , Recife, Brazil, 51010-000
The Recife JCPM Trade Center is in a 20-story building. The center has access to a helipad and five floors of parking. It is 10km from Guararapas International Airport.
Recife, Cicero Dias - Shopping Center Recife
Rua Padre Carapuceiro, 858 , Recife, Brazil, 51020-280
The business center is located in the Cicero Dias Tower, part of a prestigious complex of five buildings, the Queiroz Galvão Center. Its stands out for its distinctive architecture and for being located in an area with a consolidated entrepreneurial vocation. The business center has a privileged position in Pernambuco, in one of the greatest development axis of northeastern Brazil. The business center is walking distance to one of the Recife's greatest shopping centers and from Shopping subway station. It is also 30 minutes away from the Port of Suape, one of the most important seaports in the country and a 7-minute drive from Guararapes International Airport.
This instructor-led, live training in Recife (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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