Summary
In this chapter, we presented the specialized RNN architecture that is tailored to sequential data. We covered how RNNs work, analyzed the computational graph, and saw how RNNs enable parameter-sharing over numerous steps to capture long-range dependencies that FFNNs and CNNs are not well suited for.
We also reviewed the challenges of vanishing and exploding gradients and saw how gated units like long short-term memory cells enable RNNs to learn dependencies over hundreds of time steps. Finally, we applied RNNs to challenges common in algorithmic trading, such as predicting univariate and multivariate time series and sentiment analysis using SEC filings.
In the next chapter, we will introduce unsupervised deep learning techniques like autoencoders and generative adversarial networks and their applications to investment and trading strategies.
