Hyperparameter Optimization

So far, we have trained a neural network to predict the next 7 days of Bitcoin prices using the preceding 76 weeks of prices. On average, this model issues predictions that are about 8.4 percent distant from real Bitcoin prices.

This section describes common strategies for improving the performance of neural network models:

• Adding or removing layers and changing the number of nodes
• Increasing or decreasing the number of training epochs
• Experimenting with different activation functions
• Using different regularization strategies

We will evaluate each modification using the same active learning environment we developed by the end of the Model Evaluation section, measuring how each one of these strategies may help us develop a more precise model.

Layers and Nodes – Adding More Layers

Neural networks with single hidden layers can perform fairly well on many problems. Our first Bitcoin model (bitcoin_lstm_v0) is a good...