In the first step, we imported the required libraries. We also created a flag variable called device, which indicates whether the network will be trained using the CPU or the GPU. We structured the code in a way that GPU will be used if it is available, and CPU otherwise. Structuring the code this way has the benefit of flexibility in terms of hardware used. Bear in mind that both the data and the model should be stored on the same device!
Then, we defined a variety of parameters for both downloading the data and setting up the neural network model.
In Step 3, we downloaded the stock prices of Abercrombie and Fitch (a clothing/lifestyle company) for the years 2010-2019 and resampled the close prices to monthly frequency by ...
