Machine Translation in Python

Are you curious about the inner workings of the models that are behind products like Google Translate?

The need to pack a bilingual dictionary for your European holiday or keeping one on your desk to complete your foreign language homework is a thing of the past. You just hop on the internet and make use of a language translation service to quickly understand what the street sign means or finding out how to greet and thank a foreigner in their language. Behind the language translation services are complex machine translation models. Have you ever wondered how these models work? This course will allow you to explore the inner workings of a machine translation model. You will use Keras, a powerful Python-based deep learning library, to implement a translation model. You will then train the model to perform an English to French translation, and you will be shown techniques to improve your model. At the end of this course, you would have developed an in-depth understanding of machine translation models and appreciate them even more!

Introduction to machine translation
-In this chapter, you'll understand what the encoder-decoder architecture is and how it is used for machine translation. You will also learn about Gated Recurrent Units (GRUs) and how they are used in the encoder-decoder architecture.

Implementing an encoder decoder model with Keras
-In this chapter, you will implement the encoder-decoder model with the Keras functional API. While doing so, you will learn several useful Keras layers such as RepeatVector and TimeDistributed layers.

Training and generating translations
-In this chapter, you will train the previously defined model and then use a well-trained model to generate translations. You will see that our model does a good job when translating sentences.

Teacher Forcing and word embeddings
-In this chapter, you will learn about a technique known as Teacher Forcing, which enables translation models to be trained better and faster. Then you will learn how you can use word embeddings to make the model even better.