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 Iowa can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
IA, Des Moines - Hub Tower
699 Walnut Street, Des Moines, united states, 50309
The venue is located in the Hub Tower, right next door to Burger King and in the vicinity of the Kaleidoscope at the Hub Mall...
The venue is located in the Hub Tower, right next door to Burger King and in the vicinity of the Kaleidoscope at the Hub Mall.
IA, Johnston - Des Moines - Foxboro Square
6165 Northwest 86th Street, Des Moines, united states, 50131
The venue is located in the neighboorhood of the Community Choice Credit Union and Iowa Orthodontic Solutions, just in front ...
The venue is located in the neighboorhood of the Community Choice Credit Union and Iowa Orthodontic Solutions, just in front of the Green Meadows West Gazebo.
NE, Omaha – Linden Place
14301 First National Bank Parkway, Suite 100, Omaha, united states, 68154
The venue is located next to the First National Bank Park & Ride bus stop in the same building as Midwest Retirement Advisors...
The venue is located next to the First National Bank Park & Ride bus stop in the same building as Midwest Retirement Advisors and Principal Financial Group.
This instructor-led, live training in Iowa (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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