In this chapter, we learned about recurren6t neural networks and their aptness at processing sequential time-dependent data. The concepts that you have learned can now be applied to any time-series dataset that you may stumble upon. While this holds true for use cases such as stock market data and time-series in nature, it would be unreasonable to expect fantastic results from feeding your network real time price changes only. This is simply because the elements that affect the market price of stocks (such as investor perception, information networks, and available resources) are not nearly reflected to the level that would allow proper statistical modeling. The key is representing all relevant information in the most learnable manner possible for your network to successfully encode valuable representations therefrom.
While we did extensively explore the learning mechanisms...
