Interactive Course

Machine Translation in Python

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

  • 4 hours
  • 16 Videos
  • 58 Exercises
  • 378 Participants
  • 4,950 XP

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Course Description

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!

  1. 1

    Introduction to machine translation

    Free

    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.

  2. 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.

  3. 2

    Implementing an encoder decoder model with Keras

    Free

    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.

  4. 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.

  1. 1

    Introduction to machine translation

    Free

    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.

  2. 2

    Implementing an encoder decoder model with Keras

    Free

    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.

  3. 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.

  4. 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.

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Thushan Ganegedara
Thushan Ganegedara

Data Scientist and Author

Thushan Ganegedara is a Data Scientist. He is the author of NLP with TensorFlow (v1.6). He has over 3 years experience with TensorFlow since the inception of the TensorFlow library. Thushan likes to wear many hats as a blogger, workshop conductor, meetup speaker and a StackOverflow contributor. He is passionate about deep learning and is always looking for opportunities to learn. He occasionally enjoys swimming (not as much as he likes to). You can follow him on LinkedIn.

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Collaborators
  • Mona Khalil

    Mona Khalil

  • Ruanne Van Der Walt

    Ruanne Van Der Walt

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