Machine Learning

How to Develop a Neural Machine Translation System from Scratch

Last Updated on September 3, 2020

Develop a Deep Learning Model to Automatically
Translate from German to English in Python with Keras, Step-by-Step.

Machine translation is a challenging task that traditionally involves large statistical models developed using highly sophisticated linguistic knowledge.

Neural machine translation is the use of deep neural networks for the problem of machine translation.

In this tutorial, you will discover how to develop a neural machine translation system for translating German phrases to English.

After completing this tutorial, you will know:

  • How to clean and prepare data ready to train a neural machine translation system.
  • How to develop an encoder-decoder model for machine translation.
  • How to use a trained model for inference on new input phrases and evaluate the model skill.

Kick-start your project with my new book Deep Learning for Natural Language Processing, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Apr/2019: Fixed bug in the calculation of BLEU score (Zhongpu Chen).