diff --git a/neural_network.ipynb b/nn_puzzle_solver.ipynb
similarity index 55%
rename from neural_network.ipynb
rename to nn_puzzle_solver.ipynb
index b5c0783..e929eff 100644
--- a/neural_network.ipynb
+++ b/nn_puzzle_solver.ipynb
@@ -14,23 +14,10 @@
     }
    ],
    "source": [
-    "# If you're really new to python this code might be\n",
-    "# a bit unreadable, but I tried to make it as simple\n",
-    "# as possible.\n",
-    "\n",
     "# Setting up our imported libraries.\n",
     "import numpy as np\n",
     "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "\n",
-    "# We're going to use numpy for some easy\n",
-    "# array functionality with keras, and keras\n",
-    "# is our library for handling most of our neural network\n",
-    "# stuff. That being said, you need to have some sort of\n",
-    "# backend for keras to work with, such as the recommended\n",
-    "# TensorFlow, which I'm using here.\n",
-    "# Since keras uses those as a backend, you don't inherently\n",
-    "# need to import it."
+    "from keras.layers import Dense"
    ]
   },
   {
@@ -61,14 +48,12 @@
     }
    ],
    "source": [
-    "# Generating dummy data so I can understand how to input.\n",
+    "# Generating dummy data.\n",
+    "\n",
     "data = np.random.random((1000,240))\n",
     "output = np.random.random((1000, 29))\n",
     "\n",
-    "# Here's some printouts to see exactly what I'm doing here.\n",
-    "print(data.size)\n",
-    "print(data[0].size)\n",
-    "print(data)"
+    "# Replace this with parser code later."
    ]
   },
   {
@@ -108,77 +93,48 @@
     "# that should need to be used for this project,\n",
     "# considering that it will only be dealing with\n",
     "# a linear stack of layers of neurons.\n",
+    "\n",
     "model = Sequential()\n",
     "\n",
     "# Adding layers to the model.\n",
     "\n",
-    "# Dense is the type of layer I think, don't need\n",
-    "# to look into this more since this is all I should\n",
-    "# need to use.\n",
-    "\n",
-    "# units = the number of neurons for this layer.\n",
-    "\n",
-    "# activation = the activation function for this layer.\n",
-    "# our project doc says to use hyperbolic tangent, so\n",
-    "# I set this to tanh. Except for the output layer,\n",
-    "# which I set to sigmoid.\n",
-    "\n",
-    "# input_dim = the dimension of the input list,\n",
-    "# should only be set for the  input layer.\n",
-    "\n",
     "model.add(Dense(units=240, activation='tanh', input_dim=240))\n",
     "model.add(Dense(units=120, activation='tanh'))\n",
     "model.add(Dense(units=29, activation='sigmoid'))\n",
     "\n",
     "# Configure the learning process.\n",
-    "# \n",
-    "\n",
-    "# optimizer = I'm just using \n",
-    "# Stomchastic gradient descent for this,\n",
-    "# remember that this uses essentially staggered\n",
-    "# aggregation by step to calculate gradient\n",
-    "# descent towards our target, which is faster\n",
-    "# than doing all of the calculation together.\n",
-    "\n",
-    "# loss = the loss function, currently I'm using\n",
-    "# mean squared error, but might change to\n",
-    "# mean_absolute_percentage_error, considering\n",
-    "# I think we're supposed to calculate cost\n",
-    "# based on the percentage we are away from the\n",
-    "# correct number of moves away from the solved state.\n",
-    "\n",
-    "# metrics = evaluation metrics...\n",
-    "# I think all I care about is accuracy in this case,\n",
-    "# if anything.\n",
     "\n",
     "model.compile(optimizer='sgd',\n",
     "             loss='mean_squared_error',\n",
     "             metrics=['accuracy'])\n",
     "\n",
-    "# This is where we're configuring how we train the network:\n",
-    "\n",
-    "# data = the input sets of training data for this network,\n",
-    "# in my case I'm unsure what exactly that will be.\n",
-    "\n",
-    "# output = the input sets of target data for the network,\n",
-    "# I believe this should just be a set of the same size\n",
-    "# as the training data all containing the number\n",
-    "# of steps until being solved for each state... I think.\n",
-    "\n",
-    "# epochs = it seems like this is how many times this\n",
-    "# training should be run...\n",
-    "\n",
-    "# batch_size = I'm pretty sure this directly correlates\n",
-    "# to how many input sets we train per step.\n",
+    "# Train the network.\n",
     "\n",
     "model.fit(data, output, epochs=5, batch_size=10)\n",
     "\n",
     "# Generating predictions should look like this:\n",
     "\n",
-    "# predictions = model.predict(testing_data, batch_size=10)\n",
+    "# predictions = model.predict(testing_data, batch_size=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0xf23456789abcdef0\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data/0.bin', 'rb') as f:\n",
+    "    data = f.read()\n",
     "\n",
-    "# I've commented it out since I don't have any real\n",
-    "# data to predict yet."
+    "print(hex(int.from_bytes(data, byteorder='little', signed=False)))"
    ]
   },
   {