changed readme

README.md

@@ -53,3 +53,5 @@ To swap home and away, you could replace the variables containing all of the pla
 ## Training Process
 
 I downloaded data from stats.nba.com using the [https://github.com/swar/nba_api](swar/nba_api) package to get information on minutes played, game outcomes, and a few other dimensional elements to make everything fit together. Then, I ran a custom PyTorch training loop to train the model(s) on their chosen loss objective (spread, money line, or spread probability).
+
+I then used code roughly based on NangoGPT and TorchTune to train the model.

example.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,14 +38,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Home team win probability: 0.66\n"
+      "Home team win probability: 0.65\n"
      ]
     },
     {
@@ -54,13 +54,13 @@
        "<BarContainer object of 40 artists>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]