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 Taichung or in NobleProg corporate training centers in Taichung.
NobleProg -- Your Local Training Provider
Taichung, Global
3/F, No.179, Fuhuiyuan Blvd., Xitun Dist, Taichung City, taiwan
The Taichung Global business centre is located in the new Taichung City Government Center area. This area is the major busine...
The Taichung Global business centre is located in the new Taichung City Government Center area. This area is the major business hub of Taichung and includes the seat of Taichung City Government, many new hotels, restaurants and shopping malls such as Shin Kong Mitsukoshi Department Stores, Top City Department Stores and Warner Village Cinema. Quite a few grade A office buildings are located here, Global is the best among them.
The centre is well located. Only 1 minute walk to Taichung city hall and 5 minutes to upcoming MRT system. There are many amenities including restaurants, banks and retail facilities nearby. The Regus Taichung Global business centre offers a perfect solution for local and overseas companies who want to start or grow their businesses in Taichung, who are looking for flexible and fully-equipped offices with comprehensive services at a reasonable price.
This instructor-led, live training in Taichung (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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