Online or onsite, instructor-led live Apache SINGA training courses demonstrate through interactive discussion and hands-on practice the fundamentals and advanced topics of Apache SINGA.
Apache SINGA 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. Cologne onsite live Apache SINGA trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Theodor-Heuss-Ring 23 in Cologne. Located in a ...
Overview
Our training facilities are located at Theodor-Heuss-Ring 23 in Cologne. Located in a modern office building, our premises offer enough space for successful training courses centrally in Cologne, within walking distance of the underground station Ebertplatz.
Arrival
The NobleProg training facilities are located on the left bank of the Rhine, easily accessible from Cologne Central Station via underground lines 16 and 18. The building has a limited number of parking spaces.
Parking
Cars can be parked in the area along the Torstrasse and the nearby side streets, even if you may have to search for a moment. There is no charge for parking.
Local Services
In the area of Cologne's old town there are numerous localities and around the Cologne Cathedral, the landmark of the city, there are numerous hotels.
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.
After completing this course, delegates will:
understand SINGA’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, embedding terms, building graphs and logging
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Testimonials (2)
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
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