Interactive Course

Recurrent Neural Networks for Language Modeling in Python

Use RNNs to classify text sentiment, generate sentences, and translate text between languages.

  • 4 hours
  • 16 Videos
  • 54 Exercises
  • 2,696 Participants
  • 4,500 XP

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Course Description

Machine Learning models are based on numerical values to make predictions/classification, but how can computers deal with text data? With the huge increase of available text data, applications such as automatic document classification, text generation, and neural machine translation became possible. In this course, you will learn how to use Recurrent Neural Networks to classify text (binary and multiclass), generate phrases simulating the character Sheldon from The Big Bang Theory TV Show, and translate Portuguese sentences into English. Are you ready to start your journey into Language Models using Keras and Python? Dive in!

  1. 1

    Recurrent Neural Networks and Keras

    Free

    In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

  2. Multi-class classification

    Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

  3. RNN Architecture

    You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

  4. Sequence to Sequence Models

    This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

  1. 1

    Recurrent Neural Networks and Keras

    Free

    In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

  2. RNN Architecture

    You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

  3. Multi-class classification

    Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

  4. Sequence to Sequence Models

    This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

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David Cecchini
David Cecchini

Data Scientist

I am a Data Scientist focusing my work and research on using machine learning on text data. I entered the field when I co-founded a startup company in the field of RegTech that automatically collect, classify and distribute regulations on highly regulated markets, and am currently a Ph.D. student at Tsinghua-Berkeley Shenzhen Institute, a partner program from Tsinghua University from China and UC Berkeley from the USA.

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