Chainer Training in Minnesota

Chainer Training in Minnesota

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. Minnesota onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.

NobleProg -- Your Local Training Provider

MN, Eagan - Grand Oak I

Grand Oak
860 Blue Gentian Road Suite 200
Eagen, ME 55121
United States
,
See map: Google Maps
Maine US
MN, Eagan - Grand Oak I
Learn Chainer in our training center in Minnesota. The venue is located in the Grand Oak I in the neighboorhood of Canon Solutions America, MN Society of Professional Engineers, Learners Edge and Mendota Insurance Company. Read more

MN, Minneapolis - Fifth Street Towers

Fifth Street Towers
100 South Fifth Street 19th Floor
Minneapolis, MN 55402
United States
,
See map: Google Maps
Minnesota US
MN, Minneapolis - Fifth Street Towers
Learn Chainer in our training center in Minnesota.

Fifth Street Towers is a LEED Gold, Energy Star® certified, two-tower Class A office complex conveniently located at the confluence of bus and light rail transit systems in downtown Minneapolis, just across the street from Soo Line Building City Apartments. 

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Testimonials

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Chainer Course Events - Minnesota

Chainer Course Outlines in Minnesota

Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Minnesota (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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