Summary

In this chapter, we learned how to evaluate our model using the MSE, RMSE, and MAPE metrics. We computed the latter two metrics in a series of 19-week predictions made by our first neural network model. By doing this, we learned that it was performing well.

We also learned how to optimize a model. We looked at optimization techniques, which are 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: retrains our model periodically with new data and is able to make predictions using an HTTP API interface.