Deep Learning Quick Reference

Summary

In this chapter, we talked about using recurrent neural networks to predict the next element in a sequence. We covered both RNNs in general and LSTMs specifically, and we focused on using LSTMs to predict a time series. In order to make sure we understood the benefits and challenges of using LSTMs for time series, we briefly reviewed some basics of time series analysis. We spent a few minutes talking about traditional time series models as well, including ARIMA and ARIMAX.

Lastly, we walked through a challenging use case where we used an LSTM to predict the price of a bitcoin.

In the next chapter, we will continue to use RNNs, now focusing on natural language processing tasks and introducing the concept of embedding layers.