Local, 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 "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Southampton or in NobleProg corporate training centers in Southampton. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Learn Apache SINGA in our training center in Southampton.
This centre occupies a great position - highly visible from the M27 motorway. The building's focal point is a beautiful atrium flooding the reception area with natural light. A comfortable, professional business lounge and a large breakout area where you can relax or network add to the welcoming atmosphere. This is an outstanding south coast location next to Southampton airport with air, road and rail transport links on the doorstep. Regular launches and exhibitions held at the centre have...
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.
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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