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. Osaka onsite live Apache SINGA trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Osaka's Kokusai Centre is on the top floor of a 31-storey landmark building in Honmachi within the city's main business distr...
Osaka's Kokusai Centre is on the top floor of a 31-storey landmark building in Honmachi within the city's main business district. This centre features wide windows letting in lots of natural light. Osaka is an attractive choice for foreign enterprises seeking to enter the Japanese market, particularly in the service and IT sectors. The Kansai region is home to top global companies from the electrical equipment, chemical, food, pharmaceutical, and finance industries. In recent years, leading companies in the environmental and energy industries have set up business bases here and the Kansai region is a centre for battery production. The centre is highly accessible as it's just five minutes' walk from both Honmachi subway station and Osaka/Umeda station and Shinsaibashi/Namba station, as well as being less than 40 minutes from either Osaka International Airport or Kansai International Airport. The centre is close to many local amenities including restaurants and 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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