Added some comments

DenoisingWithAutoencoders.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First lets get our data!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -32,6 +39,13 @@
     "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Let's look at some of our data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -66,6 +80,15 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Let's create our machine learning architecture\n",
+    "\n",
+    "Here we're creating an Autoencoder architecture\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -95,6 +118,13 @@
     "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Let's Train our Model..."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -335,9 +365,16 @@
     "                callbacks=[TensorBoard(log_dir='/data/scratch/wsmonroe', histogram_freq=0, write_graph=False)])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How'd we do?"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -354,6 +391,8 @@
     }
    ],
    "source": [
+    "\n",
+    "\n",
     "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
     "\n",
     "n = 10\n",
@@ -375,6 +414,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,