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Neural Machine Translation English to German

Deep learning Keras Sequence analysis Text analysis Text processing
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Uses a character level encoder-decoder network of LSTMs. The encoder network reads the input sentence character by character and summarizes the sentence in its state. This state is then used as initial state of the decoder network to produce the translated sentence one character at a time. During prediction, the decoder also recieves its previous output as input to the next time. For training we use a technique called "teacher forcing" i.e. we feed the actual previous character instead of the previous prediction which greatly benefits the training. This example is an adaptation of the following Keras blog post to KNIME: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html In order to run the example, please make sure you have the following KNIME extensions installed: * KNIME Deep Learning - Keras Integration (Labs) You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and further information.

External resources

  • Sequence-to-sequence Keras blog

Used extensions & nodes

Created with KNIME Analytics Platform version 4.1.0
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    KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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