SageMaker-based Neural Sequence-to-Sequence (seq2seq)-based Model Training System

A SageMaker-based Neural Sequence-to-Sequence (seq2seq)-based Model Training System is a neural seq2seq training system that is a SageMaker-based training system.

• Example(s):
  • …
• Counter-Example(s):
  • an LSTM-based Language Model Training System.
  • a Bi-Directional LSTM Modeling System.
  • a Neural Machine Translation System.
• See: AWS SageMaker.

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References

2018

• https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq.html
  • QUOTE: ... Amazon SageMaker Sequence to Sequence is a supervised learning algorithm where the input is a sequence of tokens (for example, text, audio) and the output generated is another sequence of tokens. Example applications include: machine translation (input a sentence from one language and predict what that sentence would be in another language), text summarization (input a longer string of words and predict a shorter string of words that is a summary), speech-to-text (audio clips converted into output sentences in tokens). Recently, problems in this domain have been successfully modeled with deep neural networks that show a significant performance boost over previous methodologies. Amazon SageMaker seq2seq uses Recurrent Neural Networks (RNNs) and Convolutional Neural Network (CNN) models with attention as encoder-decoder architectures. ...

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