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. Boston onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
MA, Boston - Federal Street
101 Federal Street Suite 1900, Boston, united states, 02110
The office in Boston's Federal Street is a recently renovated office complex set in the heart of the financial distric...
The office in Boston's Federal Street is a recently renovated office complex set in the heart of the financial district, just one block from Congress Street. Based on the 16th and 19th floors, the office offers spectacular views of the city. Finance and tourism are some of the prime businesses in the state capital, which is the largest city in New England. With its many colleges and universities, Boston is also regarded an international center of higher education, with students contributing more than $4billion to the economy annually. It has a strong reputation for medicine, biotechnology and research. It is ranked in the top 20 of global financial centers and number one for innovation. Boston also has an historic seaport which supports tourism, industry and fishing.
This instructor-led, live training in Boston (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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