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". Manchester onsite live Apache SINGA trainings can be carried out locally on customer premises or in NobleProg corporate training centres. 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 Manchester.
The Venue in Manchester's Piccadilly district is at the centre of a transport hub within easy access of mainline and tram stations (in fact the tram stops right outside ther door!).
It is situated across the 8th floor with fantastic views over the city and an abundance of natural light. With 15 rooms of various sizes including a self contained event wing along with other smaller rooms and suites ideal for meeting and training events. All the usual touches you would expect from etc....
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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