Recurrent Neural Networks

Abstract

The previous chapter showed how a deep learning model—specifically CNNs—could be applied to images. The process could be decoupled into a feature extractor that figures out the optimal hidden-state representation of the input (in this case a vector of feature maps) and a classifier (typically a fully connected layer). This chapter focuses on the hidden-state representation of other forms of data and explores RNNs. RNNs are especially useful for analyzing sequences, which is particularly helpful for natural language processing and time series analysis.