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 Trojmiasto can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Gdynia
Hotel Nadmorski, Ejsmonda 2, Gdynia, Poland, 81-409
The training room is located just 3 kilometers from the PKP/PKS Station in Gdynia, making it easily accessible for participants traveling by train or bus. Additionally, it is only 400 meters away from the bus stop, facilitating access even for those using public transportation. It is equipped with necessary training tools such as a projector, screen, and flipchart, providing comfortable conditions for both participants and the trainer.
Gdańsk
Hotel Fahrenheit, Grodzka 19, Gdańsk, Poland, 80-841
The training room is located in the very heart of the picturesque Gdansk Old Town, making the surroundings not only inspiring but also exceptionally attractive for participants. Within close proximity, you can find the railway and bus stations, facilitating arrival for those traveling by both train and bus. Additionally, the airport and port are also within reach, making this location convenient for individuals coming from distant places, both domestically and internationally.
This instructor-led, live training in Trojmiasto (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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