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. New Jersey onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
NJ, Totowa - Riverview Drive
999 Riverview Drive, Totowa, united states, 07512
Totowa, NJ - The venue is located on the corner of Riverview Dr and Commerce Way.
NJ, Princeton - Overlook Business Center
100 Overlook Center, Princeton, united states, 08540
The venue is located just off Highway 1, in the vicinity of the American Red Cross of Central New Jersey and Wells Fargo Bank...
The venue is located just off Highway 1, in the vicinity of the American Red Cross of Central New Jersey and Wells Fargo Bank.
NJ, Newark - One Gateway Business Center
One Gateway Center Suite 2600, Newark, united states, 07102
Located in New Jersey, it is next to the Seton Hall University Law School and the Prudential Center, and a block from the pre...
Located in New Jersey, it is next to the Seton Hall University Law School and the Prudential Center, and a block from the prestigious New Jersey Performing Arts Center. The building is connected by covered walkway to Newark Penn making New York a short commute by PATH and giving good access to Boston and Washington DC.
This instructor-led, live training in New Jersey (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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