Apache SINGA Training Courses
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets.
Apache SINGA Course Outlines
|singa||Mastering Apache SINGA||21 hours||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 Introduction Installation Quick Start Programming NeuralNet Layer Param TrainOneBatch Updater Distributed Training Data Preparation Checkpoint and Resume Python Binding Performance test and Feature extraction Training on GPU Examples Feed-forward models CNN MLP RBM + Auto-encoder Vanilla RNN for language modelling Char-RNN|
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