Here is how we proceed with the recipe:
- Clone the following GitHub repository. This is a project to encourage users to try and experiment with the seq2seq neural network architecture:
git clone https://github.com/guillaume-chevalier/seq2seq-signal-prediction.git
- Given the preceding repository, consider the following functions which load and normalize the Bitcoin historical data for the USD or EUR Bitcoin value. The functions are defined in dataset.py. Training and testing data are separated according to the 80/20 rule. As a consequence, 20 percent of the testing data is the most recent historical Bitcoin values. Every example contains 40 data points of USD and then EUR data in the feature axis/dimension. Data is normalized ...
