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Bookmark or Share- Neural machine translation with a Transformer and KerasIn this tutorial you will: Prepare the data. Implement necessary components: Positional embeddings. Attention layers. The encoder and decoder. Build & train the Transformer. Generate translations. Export the model. To get the most out of this tutorial, it helps if you know about the basics of text generation and attention mechanisms.
- Neural Machine Translation by Jointly Learning to Align and TranslateNeural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
- Neural machine translation - WikipediaNeural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
- A Gentle Introduction to Neural Machine TranslationNeural machine translation, or NMT for short, is the use of neural network models to learn a statistical model for machine translation. The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.
- Neural machine translation: A review of methods, resources, and tools ...Neural machine translation has become the dominant approach to machine translation in both research and practice. This article reviewed the widely used methods in NMT, including modeling, decoding, data augmentation, interpretation, as well as evaluation.
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