RNNs for Multivariate Time Series and Sentiment Analysis
The previous chapter showed how convolutional neural networks (CNNs) are designed to learn features that represent the spatial structure of grid-like data, especially images, but also time series. This chapter introduces recurrent neural networks (RNNs) that specialize in sequential data where patterns evolve over time and learning typically requires memory of preceding data points.
Feedforward neural networks (FFNNs) treat the feature vectors for each sample as independent and identically distributed. Consequently, they do not take prior data points into account when evaluating the current observation. In other words, they have no memory.
The one- and two-dimensional convolutional filters used by CNNs can extract features that are a function of what is typically a small number of neighboring data points. However, they only allow shallow parameter-sharing: each output results from applying the same...
