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. Namur onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Namur, Hotel de Ville
NobleProg Namur, Rue de Fer, 48 , Namur, Belgium, 5000
Namur is a French-speaking city in Belgium, capital of Wallonia since 1986, and capital of the province of Namur. Occupied...
Namur is a French-speaking city in Belgium, capital of Wallonia since 1986, and capital of the province of Namur. Occupied since the Neolithic period, the confluence of the Sambre and the Meuse saw the birth of the Roman city at the beginning of our era. Former stronghold of the Counts of Namur, attached to the house of Luxembourg and then to the Habsburg Empire, the citadel of Namur was the focus of many sieges over the years. Despite the damage suffered by the conflicts, the city retains a rich movable and immovable heritage. Today the regional capital, the city is the seat of the Parliament of Wallonia, the Walloon Government and the Public Service of Wallonia, but also the host city of the International Francophone Film Festival, the fairground arts festival Namur in May, the Festival Verdur Rock.
This instructor-led, live training in Namur (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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