In this chapter, we learned how to evaluate our model using the metrics mean squared error (MSE), squared mean squared error (RMSE), and mean averaged percentage error (MAPE). We computed the latter two metrics in a series of 19 week predictions made by our first neural network model. We then learned that it was performing well.
We also learned how to optimize a model. We looked at optimization techniques typically used to increase the performance of neural networks. Also, we implemented a number of these techniques and created a few more models to predict Bitcoin prices with different error rates.
In the next chapter, we will be turning our model into a web application that does two things: re-trains our model periodically with new data, and is able to make predictions using an HTTP API interface.
