Arecurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. RNNs make use of information from the past. That way, they can make predictions for data with high temporal dependencies. This creates an internal state of the network that allows it to exhibit dynamic temporal behavior.
An RNN takes many input vectors to process them and output other vectors. Compared to a classical approach, using an RNN with Long Short-Term Memory cells (LSTMs) requires no, or very little, feature engineering. Data can be fed directly into the neural network, which acts like a black box, modeling the problem correctly. The approach here is rather simple in terms of how much data is preprocessed.
In this chapter, we will see how to develop a machine learning project using RNN implementation, called LSTM for human activity recognition (HAR), using the smartphones dataset. In short...