In this chapter, we will take an in-depth look at Recurrent Neural Networks (RNNs). In the previous chapter, we looked at Convolutional Neural Networks (CNNs), which are a powerful class of neural networks for computer vision tasks because of their ability to capture spatial relationships. The neural networks we will be studying in this chapter, however, are very effective for sequential data and are used in applications such as algorithmic trading, image captioning, sentiment classification, language translation, video classification, and so on.
In regular neural networks, all the inputs and outputs are assumed to be independent, but in RNNs, each output is dependent on the previous one, which allows them to capture dependencies in sequences, such as in language, where the next word depends on the previous word and the one before that.
We will start...
