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 Valencia or in NobleProg corporate training centers in Valencia.
NobleProg -- Your Local Training Provider
Valencian Community
NobleProg Valencia, Plaza Manuel Sanchis Guarner, 10, Valencia, spain, 46004
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of mo...
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of more than 700,000.
Among its most representative monuments are the Miguelete, the Cathedral, the Towers of Serranos and Quart, the Lonja de la Seda, declared a World Heritage Site by Unesco in 1996, and the City of Arts and Sciences.
In addition to its airport the city of Valencia has lines of rail, tram, metro and buses that circulate throughout the metropolis.
The Hotel Senator Parque Central is located in the heart of Valencia, just a few minutes from the old town. Surrounded by shops, restaurants and entertainment venues, the hotel is well-connected: in front of the property you will find a bus stop that will take you to the beaches (5 km) and Xàtiva station is only a 15-minute walk away.
This instructor-led, live training in Valencia (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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