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. Onsite live Apache SINGA training can be carried out locally on customer premises in Ghent or in NobleProg corporate training centers in Ghent.
NobleProg -- Your Local Training Provider
Ghent
Hotel NH Gent Belfort, Hoogpoort 63, Ghent, Belgium, 9000
There are 10 function rooms, 3 of them with natural daylight.
The largest room holds up to 400 people theatre-style for meet...
There are 10 function rooms, 3 of them with natural daylight.
The largest room holds up to 400 people theatre-style for meetings, or we can make it into a great space for a wedding reception.
And if you have cocktails or another special event in mind, you must let us show you our fabulous vaulted cellar.
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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