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Recurrent Neural Networks (RNN) for Language Modeling in Python

Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.

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4 Hours16 Videos54 Exercises11,236 Learners4500 XP

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Course Description

Learn How to Use RNN Modeling in Python

In this course, you will learn how to use Recurrent Neural Networks to classify text (binary and multiclass), generate phrases, and translate Portuguese sentences into English.

Machine Learning models are based on numerical values to make predictions and classifications, 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 are possible. Here, you’ll learn how RNNs in machine learning can help with this process.

Discover the Power of Recurrent Neural Networks

You’ll start this four-hour course by looking at the foundations of Recurrent Neural Networks. Exploring how information flows through a recurrent neural network, you’ll use a Keras RNN model to perform sentiment classification.

As you review RNN architecture in more detail, you’ll learn about vanishing and exploding gradient problems and how to embed layers in a language model.

Explore Language Models With Real-Life Data

Building on this knowledge, you’ll discover how you can prepare data for a multi-class classification task, exploring how these tasks differ from binary classification.

Finally, you’ll learn how to use RNN models for text generation and neural machine translation. You’ll use your knowledge of recurrent neural networks to replicate the speech of Sheldon from The Big Bang Theory and to translate Portuguese phrases into English.

This course provides an in-depth look at RNNs in machine learning, giving you the knowledge to build your skills in this area.
  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.

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    Introduction to the course
    50 xp
    Keras models: Sequential
    50 xp
    Keras models: Model
    50 xp
    Comparing the number of parameter of RNN and ANN
    50 xp
    Sentiment analysis
    100 xp
    Sequence to sequence models
    50 xp
    Introduction to language models
    50 xp
    Getting used to text data
    100 xp
    Preparing text data for model input
    100 xp
    Transforming new text
    100 xp
    Introduction to RNN inside Keras
    50 xp
    Keras models
    100 xp
    Keras preprocessing
    100 xp
    Your first RNN model
    100 xp
  2. 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.

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  3. 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.

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  4. 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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Datasets

Scripts of the TV show The Big Bang Theory.Sample of small sentences in English and Portuguese

Collaborators

chester
Chester Ismay
adriansoto
Adrián Soto
David Cecchini Headshot

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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