This project is a great deal more difficult than I imagined it would be... which is a good thing I suppose.

The first run of the program took 5 minutes, but I mis-configured something and it only printed out a percentage, and not an actual set of picks. That is what I am working on now.

To contrast, my follower code ran in seconds.

Everything is highly commented as I learn what each line does, as the starter code was "Frankensteined" from several tutorials. Here is where ChatGPT will come in handy, helping to understand the parameters and hyper parameters that make up the code.

On the bright side, it only took 2 hours to go from a blank script to a script that ran without errors to the end.

I did develop what I think is the best strategy once the semantic error (output of the wrong data) is resolved, and that is to hold back 7 days of results for the final test. In this way, the parametric tuning will affect the actual results rather than just affect the training phase results.

I ended up with a 3 LSTM layer design, as the 2 layer was having problems with the randomness of the data.

The next run has some statistics about the picks during the testing phase, excited to see them because I left them out of the initial script.

Based on the imports and configuration, this actually scratches the surface of deep learning, and if that can be done in 5 minutes with a quad core i7 with 16GB of RAM and a 960M graphics card with 700 cuda cores, how fast would it run on the laptop I am buying next year? That one has a 24 core i9 with 64GB RAM and an Nvidia 4090 with 7,000 cuda cores!

The goal is the PA pick3 and pick5, but the program should be flexible enough to run the pick 4 as well in the future.

I am probably learning more now than I will in the class I will have in machine learning, so that is worth it already, should ace that class!

Also reading a new book called "Python for Data Science" that seems to be explaining much of what tutorials assume you already know.

I'll stop learning when they put me in the ground.