Recurrent Neural Networks (RNNs) for Language Modeling with Keras

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.