Chapter 6. Recurrent Neural Networks
A RNN is a class of ANN where connections between units form a directed cycle. RNNs make use of information from the past. That way, they can make predictions in data with high temporal dependencies. This creates an internal state of the network, which allows it to exhibit dynamic temporal behavior. In this chapter, we will develop several real-life predictive models, using RNNs and their architectural variants, to make predictive analytics easier.
First, we will provide some theoretical background of RNNs. Then we will look at a few examples that will show a systematic way of implementing predictive models for image classification, sentiment analysis of movies, and spam predictions for Natural Language Processing (NLP).
Then we will show how to develop predictive models for time series data. Finally, we will see a how to develop a LSTM network for solving more advanced problems, such as human activity recognition.
Concisely, the following topics...
