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 York City onsite live Chainer trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Learn Chainer in our training center in New York City. The venue is located just opposite of Madison Av & E 38 St bus stop and Zuma NYC, in the same building as Chase Bank. Read more
Testimonials
★★★★★
★★★★★
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.
Sebastiaan Holman
Course: Machine Learning and Deep Learning
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
Big and up-to-date knowledge of leading and practical application examples.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
Machine Translated
like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
This instructor-led, live training in New York City (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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